Systems, methods, and devices for developing patient-specific spinal treatments, operations, and procedures

ABSTRACT

The disclosure herein relate to systems, methods, and devices for developing patient-specific spinal treatments, operations, and procedures. In some embodiments, systems, methods, and devices described herein for developing patient-specific spinal treatments, operations, and procedures can comprise an iterative virtuous cycle. The iterative virtuous cycle can further comprise pre-operative, intra-operative, and post-operative techniques or processes. For example, the iterative virtuous cycle can comprise imaging analysis, case simulation, implant production, case support, data collection, machine learning, and/or predictive modeling. One or more techniques or processes of the iterative virtuous cycle can be repeated.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application claims the benefit under 35 U.S.C. § 119(e) ofU.S. Provisional Patent Application No. 62/488,077, filed Apr. 21, 2017,U.S. Provisional Patent Application No. 62/518,305, filed Jun. 12, 2017,U.S. Provisional Patent Application No. 62/518,310, filed Jun. 12, 2017,U.S. Provisional Patent Application No. 62/597,035, filed Dec. 11, 2017,and U.S. Provisional Patent Application No. 62/612,260, filed Dec. 29,2017, each of which is incorporated herein by reference in its entiretyunder 37 C.F.R. § 1.57. Any and all applications for which a foreign ordomestic priority claim is identified in the Application Data Sheet asfiled with the present application are hereby incorporated by referenceunder 37 C.F.R. § 1.57.

BACKGROUND Field

The present application relates to spinal rods and surgical planning andprocedures thereof.

Description

Spinal surgery is one of the most frequently performed surgicalprocedures worldwide. Generally speaking, spinal surgery may involveimplantation of a spinal rod to correct the curvature of the spine of apatient and to prevent further deterioration. As such, the particularcurvature of the spinal rod can be a key factor in obtaining successfulresults from surgery.

SUMMARY

Various embodiments described herein relate to systems, methods, anddevices for developing patient-specific spinal treatments, operations,and procedures. In some embodiments, systems, methods, and devicesdescribed herein for developing patient-specific spinal treatments,operations, and procedures can comprise an iterative virtuous cycle. Theiterative virtuous cycle can further comprise preoperative,intraoperative, and postoperative techniques or processes. For example,the iterative virtuous cycle can comprise imaging analysis, casesimulation, implant production, case support, data collection, machinelearning, and/or predictive modeling. One or more techniques orprocesses of the iterative virtuous cycle can be repeated.

In some embodiments, a system for developing one or morepatient-specific spinal implants comprises: one or more computerreadable storage devices configured to store a plurality of computerexecutable instructions, and one or more hardware computer processors incommunication with the one or more computer readable storage devices andconfigured to execute the plurality of computer executable instructionsin order to cause the system to: access one or more medical images of aspine of a patient; simulate, on the one or more medical images,implantation of a spinal rod to a vertebral segment of interest, whereinthe simulation of implantation of the spinal rod further comprises:identifying one or more reference points along the vertebral segment ofinterest; and rotating one or more portions of the one or more medicalimages around the identified one or more reference points to obtain adesired surgical output curvature of the spine of the patient;determine, based at least in part on the simulation of implantation ofthe spinal rod, one or more dimensions of a patient-specific spinal rodfor the vertebral segment of interest, wherein the one or moredimensions of the patient-specific spinal rod comprises a diameter andcurvature thereof; generate spinal rod manufacturing or selection datainstructions, based at least in part on the determined one or moredimensions of the patient-specific spinal rod, for use by a spinal rodmanufacturing or selection apparatus for producing or selecting thepatient-specific spinal rod for the vertebral segment of interest;determine, from the one or more medical images, a length of an anteriorlongitudinal ligament for the vertebral segment of interest and a lengthof a posterior longitudinal ligament for the vertebral segment ofinterest; determine, from the one or more medical images, a length of ananterior curve for the vertebral segment of interest and a length of aposterior curve for the vertebral segment of interest; simulate, on theone or more medical images, implantation of one or more cages to one ormore intervertebral spaces within the vertebral segment of interest,wherein the simulation of implantation of the one or more cages furthercomprises: increasing a posterior height of each of the one or morecages until the length of the posterior curve substantially matches ordoes not exceed the length of the posterior longitudinal ligament; andincreasing lordosis of each of the one or more cages while maintainingthe length of the anterior curve shorter than the anterior longitudinalligament; determine, based at least in part on the simulation ofimplantation of the one or more cages, one or more dimensions of each ofone or more patient-specific cages for the vertebral segment ofinterest, wherein the one or more dimensions of each of the one or morepatient-specific cages comprises posterior height and anterior heightthereof; and generate cage manufacturing or selection data instructions,based at least in part on the determined one or more dimensions of eachof the one or more patient-specific cages, for use by a cagemanufacturing or selection apparatus for producing or selecting the oneor more patient-specific cages for the one or more intervertebralspaces, wherein the patient-specific spinal rod for the vertebralsegment of interest is produced or selected from a pre-existing range ofspinal rods based at least in part on the generated spinal rodmanufacturing or selection data instructions, and wherein the one ormore patient-specific cages for the one or more intervertebral spacesare produced or selected from a pre-existing range of cages based atleast in part on the generated cage manufacturing or selection datainstructions.

In certain embodiments, the system is further caused to: determine, fromthe one or more medical images, for each vertebra within the vertebralsegment of interest, a screw insertion axis projected length on asagittal plane and vertebral body width; determine, based at least inpart from predetermined spinal anatomical data, literature, or surgeonpreferences, for each vertebra within the vertebral segment of interest,an assumed or predetermined angulation of an implanted screw inreference to an endplate to which the screw is configured to be attachedto, an assumed or predetermined angle between a vertebra axis and apedicle axis on a transverse plane, an assumed or predetermined ratiobetween screw length and screw insertion axis length, and an assumed orpredetermined ratio between vertebral body width and pedicle width;generate one or more desired lengths of one or more patient-specificscrews for insertion in each vertebra within the vertebral segment ofinterest based at least in part on the determined screw insertion axisprojected length on the sagittal plane, the determined vertebral bodywidth, the assumed or predetermined angle between the vertebra axis andthe pedicle axis on the transverse plane, and the assumed orpredetermined ratio between screw length and screw insertion axislength; generate one or more desired diameters of the one or morepatient-specific screws for insertion in each vertebra within thevertebral segment of interest based at least in part on the determinedscrew insertion axis projected length on the sagittal plane, thedetermined vertebral body width, and the assumed or predetermined ratiobetween vertebral body width and pedicle width; and generate screwmanufacturing or selection data instructions, based at least in part onthe determined one or more desired lengths and desired diameters, foruse by a screw manufacturing or selection apparatus for producing orselecting from a pre-existing range of screws the one or morepatient-specific screws. In certain embodiments, at least one of the oneor more patient-specific screws comprises one or more sensors, whereinthe one or more sensors are configured to provide intraoperativetracking data, wherein the intraoperative tracking data comprisesorientation and position data of a portion of the spine of the patientin substantially real-time, wherein the intraoperative tracking data isconfigured to assist a surgical procedure. In certain embodiments, theone or more patient-specific screws are configured to be inserted intoone or more vertebra using a surgical tool, wherein the surgical toolcomprises one or more sensors, wherein the one or more sensors areconfigured to provide intraoperative tracking data, wherein theintraoperative tracking data comprises orientation and position data ofa portion of the spine of the patient in substantially real-time,wherein the intraoperative tracking data is configured to assist asurgical procedure.

In certain embodiments, the desired surgical output curvature of thespine is determined based at least in part on one or more predictedpost-operative parameters, wherein the system is further caused togenerate a prediction of the one or more post-operative parameters by:analyzing the one or more medical images to determine one or morepre-operative variables relating to the spine of the patient, whereinthe one or more pre-operative variables comprise at least one of UIL,LIL, age of the patient, pelvic incidence pre-operative values, pelvictilt pre-operative values, lumbar lordosis pre-operative values,thoracic kyphosis pre-operative values, or sagittal vertical axispre-operative values, and generating a prediction of one or morepost-operative variables based at least in part on applying a predictivemodel, wherein the predictive model is generated by: accessing a datasetfrom an electronic database, the dataset comprising data collected fromone or more previous patients and spinal surgical strategy employed forthe one or more previous patients, dividing the dataset into one or morecategories based on spinal surgery domain knowledge; standardizing thedata in the first subcategory; selecting a model algorithm to the datain the first subcategory; inputting a first set of input values from thefirst subcategory into the model algorithm to train the predictive modelbased on a first set of output values from the first subcategory;inputting a second set of input values from the second subcategory intothe trained predictive model and comparing results generated by thetrained predictive model with a second set of output values from thesecond subcategory; and storing the trained predictive model forimplementation, wherein the post-operative parameters comprise one ormore of pelvic tilt, lumbar lordosis, thoracic kyphosis, or sagittalvertical axis.

In certain embodiments, the one or more medical images of the spinecomprise one or more of a sagittal x-ray image, a frontal x-ray image, aflexion x-ray image, an extension x-ray image, or a MRI image. Incertain embodiments, the one or more medical images comprises one ormore two-dimensional x-ray images, and wherein the system is furthercaused to calibrate the one or more two-dimensional x-ray images andgenerate a composite three-dimensional image based on the one or moretwo-dimensional x-ray images.

In some embodiments, a system for developing one or morepatient-specific spinal implants comprises: one or more computerreadable storage devices configured to store a plurality of computerexecutable instructions; and one or more hardware computer processors incommunication with the one or more computer readable storage devices andconfigured to execute the plurality of computer executable instructionsin order to cause the system to: access one or more medical images of aspine of a patient; determine, from the one or more medical images, alength of an anterior longitudinal ligament for a vertebral segment ofinterest and a length of a posterior longitudinal ligament for thevertebral segment of interest; determine, from the one or more medicalimages, a length of an anterior curve for the vertebral segment ofinterest and a length of a posterior curve for the vertebral segment ofinterest; simulate, on the one or more medical images, implantation ofone or more cages to one or more intervertebral spaces within thevertebral segment of interest, wherein the simulation of implantation ofthe one or more cages further comprises: increasing a posterior heightof each of the one or more cages until the length of the posterior curvesubstantially matches or does not exceed the length of the posteriorlongitudinal ligament; and increasing lordosis of each of the one ormore cages while maintaining the length of the anterior curve shorterthan the anterior longitudinal ligament; determine, based at least inpart on the simulation of implantation of the one or more cages, one ormore dimensions of each of one or more patient-specific cages for thevertebral segment of interest, wherein the one or more dimensions ofeach of the one or more patient-specific cage comprises posterior heightand anterior height thereof; generate cage manufacturing or selectiondata instructions, based at least in part on the determined one or moredimensions of each of the one or more patient-specific cages, for use bya cage manufacturing or selection apparatus for producing or selectingthe one or more patient-specific cages for the one or moreintervertebral spaces, wherein the one or more patient-specific cagesfor the one or more intervertebral spaces are produced or selected froma pre-existing range of cages based at least in part on the generatedcage manufacturing or selection data instructions; determine, from theone or more medical images, for each vertebra within the vertebralsegment of interest, a screw insertion axis projected length on asagittal plane and vertebral body width; determine, based at least inpart from predetermined spinal anatomical data, literature, or surgeonpreferences, for each vertebra within the vertebral segment of interest,an assumed or predetermined angulation of an implanted screw inreference to an endplate to which the screw is configured to be attachedto, an assumed or predetermined angle between a vertebra axis and apedicle axis on a transverse plane, an assumed or predetermined ratiobetween screw length and screw insertion axis length, and an assumed orpredetermined ratio between vertebral body width and pedicle width;generate one or more desired lengths of one or more patient-specificscrews for insertion in each vertebra within the vertebral segment ofinterest based at least in part on the determined screw insertion axisprojected length on the sagittal plane, the determined vertebral bodywidth, the assumed or predetermined angle between the vertebra axis andthe pedicle axis on the transverse plane, and the assumed orpredetermined ratio between screw length and screw insertion axislength; generate one or more desired diameters of the one or morepatient-specific screws for insertion in each vertebra within thevertebral segment of interest based at least in part on the determinedscrew insertion axis projected length on the sagittal plane, thedetermined vertebral body width, and the assumed or predetermined ratiobetween vertebral body width and pedicle width; and generate screwmanufacturing or selection data instructions, based at least in part onthe determined one or more desired lengths and desired diameters, foruse by a screw manufacturing or selection apparatus for producing orselecting from a pre-existing range of screws the one or morepatient-specific screws.

In certain embodiments, at least one of the one or more patient-specificscrews comprises one or more sensors, wherein the one or more sensorsare configured to provide intraoperative tracking data, wherein theintraoperative tracking data comprises orientation and position data ofa portion of the spine of the patient in substantially real-time,wherein the intraoperative tracking data is configured to assist asurgical procedure. In certain embodiments, the one or morepatient-specific screws are configured to be inserted into one or morevertebra using a surgical tool, wherein the surgical tool comprises oneor more sensors, wherein the one or more sensors are configured toprovide intraoperative tracking data, wherein the intraoperativetracking data comprises orientation and position data of a portion ofthe spine of the patient in substantially real-time, wherein theintraoperative tracking data is configured to assist a surgicalprocedure.

In certain embodiments, the system is further caused to: simulate, onthe one or more medical images, implantation of a spinal rod to thevertebral segment of interest, wherein the simulation of implantation ofthe spinal rod further comprises: identifying one or more referencepoints along the vertebral segment of interest; and rotating one or moreportions of the one or more medical images around the identified one ormore reference points to obtain a desired surgical output curvature ofthe spine of the patient; determine, based at least in part on thesimulation of implantation of the spinal rod, one or more dimensions ofa patient-specific spinal rod for the vertebral segment of interest,wherein the one or more dimensions of the patient-specific spinal rodcomprises a diameter and curvature thereof; generate spinal rodmanufacturing or selection data instructions, based at least in part onthe determined one or more dimensions of the patient-specific spinalrod, for use by a spinal rod manufacturing or selection apparatus forproducing or selecting the patient-specific spinal rod for the vertebralsegment of interest, wherein the patient-specific spinal rod for thevertebral segment of interest is produced or selected from a range ofpre-existing spinal rods based at least in part on the generated spinalrod manufacturing data instructions.

In certain embodiments, the desired surgical output curvature of thespine is determined based at least in part on one or more predictedpost-operative parameters, wherein the system is further caused togenerate a prediction of the one or more post-operative parameters by:analyzing the one or more medical images to determine one or morepre-operative variables relating to the spine of the patient, whereinthe one or more pre-operative variables comprise at least one of UIL,LIL, age of the patient, pelvic incidence pre-operative values, pelvictilt pre-operative values, lumbar lordosis pre-operative values,thoracic kyphosis pre-operative values, or sagittal vertical axispre-operative values; and generating a prediction of one or morepost-operative variables based at least in part on applying a predictivemodel, wherein the predictive model is generated by: accessing a datasetfrom an electronic database, the dataset comprising data collected fromone or more previous patients and spinal surgical strategy employed forthe one or more previous patients; dividing the dataset into one or morecategories based on spinal surgery domain knowledge; standardizing thedata in the first subcategory; selecting a model algorithm to the datain the first subcategory; inputting a first set of input values from thefirst subcategory into the model algorithm to train the predictive modelbased on a first set of output values from the first subcategory;inputting a second set of input values from the second subcategory intothe trained predictive model and comparing results generated by thetrained predictive model with a second set of output values from thesecond subcategory; and storing the trained predictive model forimplementation, wherein the post-operative parameters comprise one ormore of pelvic tilt, lumbar lordosis, thoracic kyphosis, or sagittalvertical axis.

In certain embodiments, the one or more medical images of the spinecomprise one or more of a sagittal x-ray image, a frontal x-ray image, aflexion x-ray image, an extension x-ray image, or a MRI image. Incertain embodiments, the one or more medical images comprises one ormore two-dimensional x-ray images, and wherein the system is furthercaused to calibrate the one or more two-dimensional x-ray images andgenerate a composite three-dimensional image based on the one or moretwo-dimensional x-ray images.

In some embodiments, a system for developing one or morepatient-specific spinal implants comprises: one or more computerreadable storage devices configured to store a plurality of computerexecutable instructions; and one or more hardware computer processors incommunication with the one or more computer readable storage devices andconfigured to execute the plurality of computer executable instructionsin order to cause the system to: access one or more medical images of aspine of a patient; determine, from the one or more medical images, aheight of each of one or more discs on a vertebral segment of interest;determine, for each of the one or more discs on the vertebral segment ofinterest, disc height as a percentage of total disc height of thevertebral segment of interest and/or disc angulation as a percentage oftotal disc angulation of the vertebral segment of interest; analyze thedetermined disc height percentage and/or determined disc angulationpercentage of each of the one or more discs on the vertebral segment ofinterest by comparing the determined disc height percentage and/ordetermined disc angulation percentage of each of the one or more discson the vertebral segment of interest with predetermined disc heightpercentages and/or predetermined disc angulation percentages of one ormore corresponding discs of an asymptomatic population, wherein thepredetermined disc height percentages and/or the predetermined discangulation percentages of one or more corresponding discs of anasymptomatic population are updated periodically and/or continuously;simulate, on the one or more medical images, implantation of one or morecages to one or more intervertebral spaces within the vertebral segmentof interest based at least in part on the comparison of the determineddisc height percentage and/or the determined disc angulation percentageof each of the one or more discs on the vertebral segment of interestwith predetermined disc height percentages and/or predetermined discangulation percentages of the one or more corresponding discs of theasymptomatic population; determine, based at least in part on thesimulation of implantation of the one or more cages, one or moredimensions of each of one or more patient-specific cages for thevertebral segment of interest, wherein the one or more dimensions ofeach of the one or more patient-specific cage comprises posteriorheight, anterior, and/or angulation height thereof, generate cagemanufacturing or selection data instructions, based at least in part onthe determined one or more dimensions of each of the one or morepatient-specific cages, for use by a cage manufacturing or selectionapparatus for producing or selecting the one or more patient-specificcages for the one or more intervertebral spaces, wherein the one or morepatient-specific cages for the one or more intervertebral spaces areproduced or selected from a pre-existing range of cages based at leastin part on the generated cage manufacturing or selection datainstructions; determine, from the one or more medical images, for eachvertebra within the vertebral segment of interest, a screw insertionaxis projected length on a sagittal plane and vertebral body width;determine, based at least in part from predetermined spinal anatomicaldata, literature, or surgeon preferences for each vertebra within thevertebral segment of interest, an assumed or predetermined angulation ofan implanted screw in reference to an endplate to which the screw isconfigured to be attached to, an assumed or predetermined angle betweena vertebra axis and a pedicle axis on a transverse plane, an assumed orpredetermined ratio between screw length and screw insertion axislength, and an assumed or predetermined ratio between vertebral bodywidth and pedicle width, generate one or more desired lengths of one ormore patient-specific screws for insertion in each vertebra within thevertebral segment of interest based at least in part on the determinedscrew insertion axis projected length on the sagittal plane, thedetermined vertebral body width, the assumed or predetermined anglebetween the vertebra axis and the pedicle axis on the transverse plane,and the assumed or predetermined ratio between screw length and screwinsertion axis length; generate one or more desired diameters of the oneor more patient-specific screws for insertion in each vertebra withinthe vertebral segment of interest based at least in part on thedetermined screw insertion axis projected length on the sagittal plane,the determined vertebral body width, and the assumed or predeterminedratio between vertebral body width and pedicle width; and generate screwmanufacturing or selection data instructions, based at least in part onthe determined one or more desired lengths and desired diameters, foruse by a screw manufacturing or selection apparatus for producing orselecting from a pre-existing range of screws the one or morepatient-specific screws.

In certain embodiments, at least one of the one or more patient-specificscrews comprises one or more sensors, wherein the one or more sensorsare configured to provide intraoperative tracking data, wherein theintraoperative tracking data comprises orientation and position data ofa portion of the spine of the patient in substantially real-time,wherein the intraoperative tracking data is configured to assist asurgical procedure.

In certain embodiments, the system is further caused to: simulate, onthe one or more medical images, implantation of a spinal rod to thevertebral segment of interest, wherein the simulation of implantation ofthe spinal rod further comprises: identifying one or more referencepoints along the vertebral segment of interest; and rotating one or moreportions of the one or more medical images around the identified one ormore reference points to obtain a desired surgical output curvature ofthe spine of the patient; determine, based at least in part on thesimulation of implantation of the spinal rod, one or more dimensions ofa patient-specific spinal rod for the vertebral segment of interest,wherein the one or more dimensions of the patient-specific spinal rodcomprises a diameter and curvature thereof; generate spinal rodmanufacturing or selection data instructions, based at least in part onthe determined one or more dimensions of the patient-specific spinalrod, for use by a spinal rod manufacturing or selection apparatus forproducing or selecting the patient-specific spinal rod for the vertebralsegment of interest, wherein the patient-specific spinal rod for thevertebral segment of interest is produced or selected from a range ofpre-existing spinal rods based at least in part on the generated spinalrod manufacturing data instructions.

In certain embodiments, the desired surgical output curvature of thespine is determined based at least in part on one or more predictedpost-operative parameters, wherein the system is further caused togenerate a prediction of the one or more post-operative parameters by:analyzing the one or more medical images to determine one or morepre-operative variables relating to the spine of the patient, whereinthe one or more pre-operative variables comprise at least one of UIL,LIL, age of the patient, pelvic incidence pre-operative values, pelvictilt pre-operative values, lumbar lordosis pre-operative values,thoracic kyphosis pre-operative values, or sagittal vertical axispre-operative values; and generating a prediction of one or morepost-operative variables based at least in part on applying a predictivemodel, wherein the predictive model is generated by: accessing a datasetfrom an electronic database, the dataset comprising data collected fromone or more previous patients and spinal surgical strategy employed forthe one or more previous patients; dividing the dataset into one or morecategories based on spinal surgery domain knowledge; standardizing thedata in the first subcategory; selecting a model algorithm to the datain the first subcategory; inputting a first set of input values from thefirst subcategory into the model algorithm to train the predictive modelbased on a first set of output values from the first subcategory;inputting a second set of input values from the second subcategory intothe trained predictive model and comparing results generated by thetrained predictive model with a second set of output values from thesecond subcategory; and storing the trained predictive model forimplementation, wherein the post-operative parameters comprise one ormore of pelvic tilt, lumbar lordosis, thoracic kyphosis, or sagittalvertical axis.

In certain embodiments, the one or more medical images of the spinecomprise one or more of a sagittal x-ray image, a frontal x-ray image, aflexion x-ray image, an extension x-ray image, or a MRI image. Incertain embodiments, the one or more medical images comprises one ormore two-dimensional x-ray images, and wherein the system is furthercaused to calibrate the one or more two-dimensional x-ray images andgenerate a composite three-dimensional image based on the one or moretwo-dimensional x-ray images.

In some embodiments, a system for providing intraoperative tracking toassist spinal surgery comprises: two or more active sensors, whereineach of the two or more active sensors comprises one or moreaccelerometers and/or one or more gyroscopes; two or more attachmentdevices, wherein each of the two or more attachment devices comprisesone or more of said active sensors, a power source, and a wirelesstransmitter, wherein the two or more attachment devices are configuredto be attached to two or more vertebrae in a configuration such that twoof three axes of position data to be collected by the two or more activesensors are on a plane assumed to be parallel, or substantially parallelwith a determinate angle, to a sagittal plane of a patient for spinalsurgery, wherein when the two or more attachment devices are attached totwo or more vertebrae during spinal surgery, each of the two or moreactive sensors are configured to provide position and/or orientationdata of each of the two or more vertebrae to which each of the two ormore attachment devices are attached to; one or more computer readablestorage devices configured to store a plurality of computer executableinstructions; and one or more hardware computer processors incommunication with the one or more computer readable storage devices andconfigured to execute the plurality of computer executable instructionsin order to cause the system to: receive the position and/or orientationdata from the wireless transmitter of each of the two or more attachmentdevices in substantially real-time, dynamically determine, based atleast in part on the position and/or orientation data and using gravityas a common reference among the position and/or orientation datareceived from the wireless transmitter of each of the two or moreattachment devices, the position and/or orientation of the two or morevertebrae to which the two or more attachment devices are attached to;dynamically generate one or more performance metrics for spinal surgerybased at least in part on comparing the determined position andorientation of the two or more vertebrae to which the two or moreattachment devices are attached to with a predetermined surgical plan;and dynamically generate, based at least in part on the generated one ormore performance metrics, guidance instructions for performing spinalsurgery.

In certain embodiments, the one or more active sensors are configured tobe an inertial measurement unit with six degrees of freedom. In certainembodiments, the one or more active sensors are configured to be aninertial measurement unit with nine degrees of freedom. In certainembodiments, the two or more attachment devices comprises a vertebralanchor. In certain embodiments, the two or more attachment devicescomprises a surgical tool. In certain embodiments, the two or moreattachment devices comprises a vertebral screw. In certain embodiments,the vertebral screw is a mono-axial screw comprising at least onesensor. In certain embodiments, the vertebral screw is a poly-axialscrew comprising at least one sensor.

For purposes of this summary, certain aspects, advantages, and novelfeatures of the invention are described herein. It is to be understoodthat not necessarily all such advantages may be achieved in accordancewith any particular embodiment of the invention. Thus, for example,those skilled in the art will recognize that the invention may beembodied or carried out in a manner that achieves one advantage or groupof advantages as taught herein without necessarily achieving otheradvantages as may be taught or suggested herein.

All of these embodiments are intended to be within the scope of theinvention herein disclosed. These and other embodiments will becomereadily apparent to those skilled in the art from the following detaileddescription having reference to the attached figures, the invention notbeing limited to any particular disclosed embodiment(s).

BRIEF DESCRIPTION OF THE DRAWINGS

A better understanding of the devices and methods described herein willbe appreciated upon reference to the following description inconjunction with the accompanying drawings, wherein:

FIG. 1 is a flowchart illustrating an overview of an exampleembodiment(s) of an iterative virtuous cycle for developingpatient-specific spinal treatments, operations, and procedures;

FIG. 2 is a flowchart illustrating an example embodiment(s) ofpre-operative imaging analysis and/or case simulation for developingpatient-specific spinal treatments, operations, and procedures;

FIG. 3 is a flowchart illustrating an example embodiment(s) of implantproduction, case support, and/or data collection for developingpatient-specific spinal treatments, operations, and procedures;

FIG. 4 is a flowchart illustrating an example embodiment(s) of datacollection, machine learning, and/or predictive modeling for developingpatient-specific spinal treatments, operations, and procedures;

FIG. 5 illustrates an example embodiment(s) of case simulation deliveryfor developing patient-specific spinal treatments, operations, andprocedures;

FIG. 6 illustrates an example embodiment(s) of case simulation deliveryfor developing patient-specific spinal treatments, operations, andprocedures;

FIGS. 7A and 7B illustrate example embodiments of spinal rods that canbe produced and/or selected using certain embodiment(s) of systems,devices, and methods for patient-specific spinal treatments, operations,and procedures;

FIG. 7C illustrates an example embodiment of a cage(s) that can beproduced and/or selected using certain embodiment(s) of systems,devices, and methods for patient-specific spinal treatments, operations,and procedures;

FIG. 7D illustrates an example embodiment of a screw(s) that can beproduced and/or selected using certain embodiment(s) of systems,devices, and methods for patient-specific spinal treatments, operations,and procedures;

FIG. 8 is a flowchart illustrating an example embodiment(s) of cageand/or screw design, production, modification, and/or selection;

FIG. 9A is a flowchart illustrating an example embodiment(s) of cagedesign, production, modification, and/or selection;

FIG. 9B illustrates an example embodiment(s) of cage design, production,modification, and/or selection;

FIG. 9C illustrates an example embodiment(s) of cage design, production,modification, and/or selection;

FIG. 10A is a flowchart illustrating an example embodiment(s) of cagedesign, production, modification, and/or selection;

FIG. 10B illustrates an example embodiment(s) of cage design,production, modification, and/or selection;

FIG. 10C illustrates an example embodiment(s) of cage design,production, modification, and/or selection;

FIG. 11A is a flowchart illustrating an example embodiment(s) of cagedesign, production, modification, and/or selection;

FIG. 11B is a schematic diagram illustrating an example embodiment(s) ofcage design, production, modification, and/or selection;

FIG. 12A is a flowchart illustrating an example embodiment(s) of screwdesign, production, modification, and/or selection;

FIG. 12B is a schematic diagram illustrating certain aspect(s) of anexample embodiment(s) of screw design, production, modification, and/orselection;

FIG. 13A is a schematic illustrating an example embodiment(s) ofintraoperative tracking;

FIGS. 13B-13G illustrate example embodiments of screws and/or sensorsthat can be used for intraoperative tracking;

FIGS. 14A-14E illustrate example embodiments of tools and/or sensorsthat can be used for intraoperative tracking;

FIG. 15 is a flowchart illustrating an example embodiment(s) ofpredictive modeling;

FIG. 16 is a schematic diagram illustrating an embodiment of a systemfor developing patient-specific spinal treatments, operations, andprocedures; and

FIG. 17 is a block diagram depicting an embodiment of a computerhardware system configured to run software for implementing one or moreembodiments of a system for developing patient-specific spinaltreatments, operations, and procedures.

DETAILED DESCRIPTION

Although several embodiments, examples, and illustrations are disclosedbelow, it will be understood by those of ordinary skill in the art thatthe inventions described herein extend beyond the specifically disclosedembodiments, examples, and illustrations and includes other uses of theinventions and obvious modifications and equivalents thereof.Embodiments of the inventions are described with reference to theaccompanying figures, wherein like numerals refer to like elementsthroughout. The terminology used in the description presented herein isnot intended to be interpreted in any limited or restrictive mannersimply because it is being used in conjunction with a detaileddescription of certain specific embodiments of the inventions. Inaddition, embodiments of the inventions can comprise several novelfeatures and no single feature is solely responsible for its desirableattributes or is essential to practicing the inventions hereindescribed.

Spinal surgery is one of the most frequently performed surgicalprocedures worldwide. Generally speaking, spinal surgery may involveimplantation of one or more implants, such as spinal rod(s), cage(s),and/or one or more screw(s) to correct the curvature of the spine of apatient and to prevent further deterioration. As such, correspondencebetween one or more spinal implants and patient anatomy can be a keyfactor in obtaining successful results from surgery. In particular, theparticular curvature, dimensions, shape and/or size of one or morespinal rods, cages, and/or screws can be crucial to obtain successfulsurgical results.

Various embodiments described herein relate to systems, methods, anddevices for developing patient-specific spinal treatments, operations,and procedures. In some embodiments, systems, methods, and devicesdescribed herein for developing patient-specific spinal treatments,operations, and procedures can comprise an iterative virtuous cycle. Theiterative virtuous cycle can further comprise preoperative,intraoperative, and postoperative techniques or processes. For example,the iterative virtuous cycle can comprise imaging analysis, casesimulation, implant production, case support, data collection, machinelearning, and/or predictive modeling. One or more techniques orprocesses of the iterative virtuous cycle can be repeated.

In particular, there can be a desired surgical outcome that isparticular to each patient. For example, based on the current state of apatient's spine, it can be known from past data, experience, and/orliterature, that a particular patient's spine should be corrected in acertain way and/or degree. In turn, in order to obtain such correctiveresults, it can be advantageous to design, generate, and/or otherformulate specific dimensions and/or other variables pertaining to oneor more implants that are specific to the particular patient. Forexample, there can be one or more desirable variables and/or parametersfor one or more spinal rods, cages, and/or screws for implantation for aspecific patient. As such, certain systems, devices, and methodsdescribed herein are configured to utilize one or more medical images ofa patient and analyze the same to determine one or more desiredparameters and/or variables of one or more spinal rods, cages, and/orscrews for implantation. Based on the determined one or more desiredparameters and/or variables, certain systems, devices, and methodsdescribed herein can be further configured to manufacture, produce,modify, select, provide guidance for selection of, and/or generateinstructions to manufacture, produce, modify, and/or select one or morespinal rods, cages, and/or screws that are specifically customized for aparticular patient.

In addition to designing, producing, and/or otherwise obtaining an idealor desired spinal implant, it can be equally, if not more, importantthat such implant is correctly implanted according to a desired and/orpredetermined surgical plan. In other words, even if one or more spinalrods, cages, and/or screws are produced, selected, or otherwise obtainedfor a specific patient, its effects can be limited if the implantationor other surgical procedure is not conducted according to a desired orpredetermined plan. As such, it can be advantageous to be able to ensureor at least increase the chances that surgery or a procedure thereof isperformed as desired. To such effect, certain systems, devices, andmethods described herein provide intraoperative tracking to provideguidance and/or performance evaluation during spinal surgery.

Further, although every patient is different and can have unique spinalconditions, some spinal conditions can be more similar than others.Also, certain individual characteristics of patients' spinal conditionscan be more similar than others. As such, it can be advantageous to beable to analyze data relating to specific patient spinal conditionspre-operation and/or post-operation and utilize the same in order topredict the outcome of spinal surgery for a new patient. The predictiveanalysis can also be used in generating a patient-specific surgicalplan, which can comprise one or more parameters and/or variables for oneor more spinal rods, cages, and/or screws. Accordingly, certain systems,methods, and devices disclosed herein are configured to utilizepredictive modeling to generate predictive surgical outcome(s) and/orpatient-specific surgical plan(s).

Iterative Virtuous Cycle

FIG. 1 is a flowchart illustrating an overview of an exampleembodiment(s) of an iterative virtuous cycle for developingpatient-specific spinal treatments, operations, and procedures. Asillustrated in FIG. 1, some embodiments of the systems, methods, anddevices comprise one or more processes that can form an iterativevirtuous cycle. For example, an iterative virtuous cycle can compriseone or more of the following: (1) imaging analysis; (2) case simulation;(3) implant production; (4) case support; (5) data collection; (6)machine learning; and/or (7) predictive modeling. Certain embodimentsmay comprise any subset of the aforementioned processes. Further, one ormore processes or techniques of a virtuous iterative cycle can berepeated.

Certain processes or techniques of the virtuous iterative cycle can beperformed at different points in time. For example, imaging analysis,case simulation, and/or implant production can be performedpre-operation. Case support and/or data collection may be performedduring operation or intra-operation. Lastly, some data collection,machine learning, and/or predictive modeling can be performedpost-operation. The whole virtuous iterative cycle and/or portionsthereof can be repeated for the same and/or different patient in certainembodiments.

FIG. 2 is a flowchart illustrating an example embodiment(s) ofpre-operative imaging analysis and case simulation for developingpatient-specific spinal treatments, operations, and procedures. Imaginganalysis can relate to analysis of one or more medical images. Forexample, one or more x-ray images of a patient, such as a frontal x-rayand sagittal x-ray image, may be analyzed. In other embodiments, the oneor more medical images can comprise an MRI scan, other x-ray image, CTscan, and/or any other medical image. The analysis of the one or moremedical images can comprise drawing one or more rod designs as anoverlay onto the one or more medical images. For example, in someembodiments, one or more rods with particular curvatures can be drawn asan overlay onto one or more x-ray images.

As illustrated in FIG. 2, in some embodiments, a medical imaging systemcan be configured to obtain and/or access one or more medical images ofa patient at block 202. The one or more medical images can betwo-dimensional and/or three-dimensional. The one or more medical imagescan be a sagittal view x-ray, a frontal view x-ray, a flexion x-ray,extension x-ray, a CT scan, and/or MRI scan of the spine of a patient.The medical facility client system can be configured to access and/orobtain the one or more medical images for output to a main server systemfor developing patient-specific spinal treatments, operations, and/orprocedures.

At block 204, the main server system can be configured to receive and/oraccess the one or more medical images from the medical facility clientsystem. For example, the one or more medical images can beelectronically transmitted over the Internet in some embodiments. Inother embodiments, one or more medical images can be inputted to themain server system offline, for example through a portable electronicstorage medium, image film, or the like.

In embodiments in which CT and/or MRI scans are utilized, a completethree-dimensional reconstruction of the spine may be obtained directlyfrom the CT and/or MRI scan itself. This can be useful for furtheranalysis, such as determining one or more parameters and/or variablesfrom the medical image(s), to generate appropriate and/or desired spinaltreatments, operations, and/or procedures specific to a patient.However, CT or MRI scans may not be available for all patients due tothe added expense and general availability of such modalities.

Rather, in some embodiments, the system can be configured to utilize oneor more x-ray images, which are more widely available, to determine oneor more parameters and/or variables in order to generate appropriateand/or desired spinal treatments, operations, and/or procedures specificto a patient. In some embodiments, the system is configured to analyzeone or more two-dimensional x-ray images, such as sagittal view, frontview, flexion, and/or extension images, separately and/or in combinationfor further analysis, for example to determine one or more parametersand/or variables. In certain embodiments, the system can be configuredto combine one or more two-dimensional x-ray images, such as thosedescribe above, to obtain a three-dimensional reconstruction of thespine for further analysis. For example, the system can be configured tocombine a frontal x-ray view and a sagittal x-ray view for flexionand/or extension states. In other embodiments, the system can beconfigured to utilize a combination of one or more x-ray, MRI, and/or CTimages.

In order to effectively utilize a plurality of medical images, thesystem can be configured to calibrate the plurality of medical images atblock 206. More specifically, a common point of reference among theplurality of medical images can be identified and used for calibrationand analysis. The system can be configured to identify a common point ofreference among a plurality of medical images either automaticallyand/or by manual input from a user. For example, a central point of thesacrum endplate or vertebra can be identified as a common point ofreference on a plurality of medical images. In particular, the centerpoint of the sacrum vertebra on a frontal view x-ray image and asagittal view x-ray image can be identified.

Further, in certain embodiments, the plurality of medical images can bescaled for consistency among the plurality of images. The calibrationprocess can comprise identifying a common part, portion, and/or shape ofthe spine of the patient in a plurality of x-ray images and adjustingthe scale of one or more of the plurality of x-ray images based on suchcommonality. For example, if the L3 plate length is chosen as a commonfeature, the length of the L3 plate can be measured on each of aplurality of x-ray images, and the x-ray images can be scaled to matchsuch length.

The calibration process or technique can be fully or partiallyautomated. In some embodiments, the calibration and/or adjustmentprocesses can be automated by extracting the colon spine and/ormeasuring the central plate for example. After a plurality of medicalimages are calibrated, the system can be configured to generate acomposite three-dimensional image at block 208 for further analysis. Inother embodiments, a single medical image or scan is used, thereby notrequiring any calibration.

The system can be configured to analyze the one or more medical imagesat block 210. Image analysis can comprise identifying one or moreendplates, identifying the distance and/or curvature between one or moreendplates, drawing one or more lines between one or more endplates, orthe like. The one or more parameters can be obtained by the systemautomatically and/or by manual input. In addition, image analysis canfurther comprise modifying one or more medical images, for example byrotating one or more portions thereof, for example around an identifiedreference point and/or axis, to mimic a desired surgical outcome of thespine. In some embodiments, one or more image analysis processes can beperformed prior to and/or after image modification such as rotation ofone or more portions of the one or more medical images.

Analysis of the one or more medical images can depend on a number offactors, such as parameters and/or variables identified by surgeons,literature, and/or a historical database of the system. Accordingly, asillustrated in FIG. 2, the system at block 210 can be configured tocommunicate with one or more of a surgeon database 212, literaturedatabase 214, plan database 216, and/or operation database 218 inanalyzing the one or more medical images. Each or all of the surgeondatabase 212, literature database 214, plan database 216, and/oroperation database 218 can be configured to be updated continuouslyand/or periodically.

The surgeon database 212 can comprise preferences of a particularsurgeon, for example certain surgical procedures or choices a particularsurgeon routinely utilizes or favors and certain parameters of the spinethat may be required to meet such surgeon preferences. Based on suchdetermination, the system can be configured to analyze the one or moremedical images to meet the preferences of a particular surgeon forexample.

The literature database 214 can comprise one or more medicalliteratures, for example related to spinal surgery. As medical researchis developed, additional parameters of the spine can be identified asbeing helpful in surgical planning. In addition, more real-life patientdata can be made available, which can include pre-operative and/orpost-operative data. As such, in certain embodiments, the literaturedatabase 214 can be used by the system to identify certain parameters,variables, and/or additional data of the spine that the system canutilize in analyzing the one or more medical images.

The plan database 216 and/or operation database 218 can compriseinternal data of the system. In other words, the plan database 216 cancomprise data related to one or more previous spinal surgical plansdeveloped by the system. Similarly, the operation database 218 cancomprise data related to actual spinal surgeries previously performed,including surgeries performed utilizing the system. Such data related topreviously planned and/or executed spinal surgeries can also provide oridentify parameters for the system to identify and/or determine as partof analyzing the one or more medical images.

After image analysis, surgical planning can be performed as part of casesimulation at block 220. Surgical planning can comprise, for example,the design, production, modification, and/or selection of one or morespinal rods, screws, and/or cages to be used for a particular patient,for example based on the analysis of one or more medical images. Morespecifically, in certain embodiments, a surgical plan generated by thesystem can comprise one or more of the length, diameter, curvature,angulation, and/or other dimension of an implant, such as a spinal rod,screw, and/or cage. For example, the system can be configured to drawand/or allow a user to draw a spinal rod of a particular curvature as anoverlay onto the one or more medical images. In particular, in someembodiments, the system can be configured to draw a spline on the spineon one or more medial images and obtain a global measurement of thespine as drawn. The system can be also configured to obtain local data,such as the corner of every vertebra for analysis. In certainembodiments, the system can be configured to generate a surgical plancomprising design and/or selection of a range, root, length and/ordiameter of one or more spinal screws. Moreover, in certain embodiments,the generated surgical plan can comprise a design, shape, size,material, or the like of specific implant, such as a body device orcage.

Similar to image analysis, the system can be configured to communicatewith one or more of a surgeon database 212, literature database 214,plan database 216, and/or operation database 218 in conducting surgicalplanning. For example, the system can be configured to take into accountsurgeon-specific preferences in planning. The system can also beconfigured to consider medical literature. The system can also beconfigured to consider its internal database comprising previouslydetermined and executed surgical plans and operations. The system can beconfigured to identify one or more previous spinal surgeries conductedutilizing the system that may be similar and/or other provide guidanceto the current patient case, based on age, severity of deformation, bonestrength or density, or the like. The system can be configured to lookin one or more databases to identify similar cases that were previouslyperformed, identify the surgical strategies previously used in thosecases, and identify the results to assist in planning. For example, insome embodiments, the system can take into account the trajectory and/orinsertion depth of a screw(s) and/or the position of cages. Morespecifically, the trajectory and/or insertion depth of a screw from oneor more previous surgeries can be used as an input in determining adesired screw length, trajectory, and/or insertion depth for a currentsurgery. In some embodiments, the system can be configured to determinea recommended implant design, positioning, and/or material(s) for aspinal rod, screw(s), or cage(s) based on previously collected data. Forexample, the previously collected data may be selected from certaincases comprising particular clinical results and/or bone quality and/orage of a patient.

In some embodiments, the generated surgical plan can comprise aparticular design and/or curvature of a spinal rod for a particularpatient, including rod diameter, material, length, curvature or thelike. For example, for the upper vertebrae, the system may recommendusing a cervical thoracic rod(s). In some embodiments, only one plan isdesigned and provided to a surgeon. In other embodiments, more than oneplan can be designed and provided to a surgeon for final determination.For example, the number of surgical plans developed by a system for asingle set of medical images of a single patent can comprise two, three,four, five, six, seven, eight, nine, and/or ten plans. In someembodiments, the system can be configured to develop and provide asurgeon with three plans. For example, the system can be configured toprovide a first plan based on surgeon-inputted strategy and/orobjectives. A second plan generated by the system can be based purely onscientific literature, and a third plan can be based on internal data ofpreviously planned surgeries by the system. In particular, previouslycollected data can be analyzed to determine which strategies or planshad high success rates, over 90% for example, based on clinical outcomessuch as angulation measurements for similar patients. The system can befurther configured to receive a selection from a surgeon between one ormore plans developed for a single patient. In other words, the systemcan be configured to generate a plurality of alternative surgical plansfor a single patient.

In some embodiments, the system can be configured to provide casesimulation guidance, for example to a surgeon, as part of developingpatient-specific spinal treatments, operations, and procedures. Incertain embodiments, the system can be configured to generate one ormore surgical plans based on a surgeon's strategy and/or objectives thatmay have been previously inputted into the system. Additionally oralternatively, the surgical plan design can be data-driven, for examplebased on scientific literature and/or other data from previoussurgeries. The scientific literature can be from 10-20 or more surgeonsand/or can be continuously and/or periodically updated. The scientificliterature can also be based on internal data collected from previouscases generated by the system. The system can also utilizemachine-learning in some embodiments to continuously improve itssurgical planning based on previous internal data and/or scientificliterature. Surgical planning can also be based on big data and mayutilize one or more big data processing techniques.

Objectives of spinal realignment can include, for example,surgeon-inputted guidelines, correction of thoracic kyphosis,age-related objectives, and/or future theory around sitting position.For example, growth of the spine can be considered in the planning stagefor younger patients. Further, surgical balance of a patient may bedependent on the age of the patient. Case simulation capabilities of thesystem can include surgeon-inputted guidelines and/or strategy, strategybased on Smith-Petersen Osteotomy (SPO)(s), strategy based on PedicleSubtraction Osteotomy (PSO) and SPO(s), strategy based on cage(s),and/or strategy based on cage(s) and SPO(s), and/or any combination ofthe above. Additional simulations can include Spondylolisthesiscorrection, compensatory mechanism of the thoracic area, and/orcompression/distraction as requested and/or necessary. Some examples ofsurgeon inputs can include a type of spinal surgery, insertion of screwsat certain portions, conducting a PSO at a certain level, conducting anSPO at a certain level, insertion of a cage at a particular portion, orthe like.

In some embodiments, a surgical plan generated by the system cancomprise one or more pre-operative images and/or surgical plan images.FIGS. 5 and 6 illustrate example embodiments of case simulation deliveryfor developing patient-specific spinal treatments, operations, andprocedures. As illustrated in FIGS. 5 and 6, as part of the planningprocess, the system can be configured to produce one or more planimages, in which a spinal rod, one or more screws, and/or one or morecages can be depicted on one or more modified or analyzed pre-operativeimages. For example, as part of the planning process, the system can beconfigured to partition a medical image into one or more portions androtate such portions with respect to one another to obtain a depictionof post-operative results or objectives. In certain embodiments, thesystem is configured to identify each vertebra and rotate the same toobtain a post-operative estimated result or curvature of the spine.Rotation of each vertebra and/or one or more portions of a medical imagethat may comprise one or more vertebrae can be data-driven based onhistorical data and/or automated in certain embodiments. An overlay of aspinal rod can be added onto such planning image to provide a visualdepiction of the surgical plan.

The surgical plan can also comprise one or more pre-operative and/orplanned spinopelvic parameters, such as pelvic tilt (PT), pelvicincidence (PT), sacral slope (SS), lumbar lordosis (LL), PI-LL, thoracickyphosis (TK), T1 pelvic angle, and/or sagittal vertical axis (SVA). Thesurgical plan can also comprise one or more cervical parameters,surgical steps, implant requirements, such as screws and/or rod type.

Referring back to FIG. 2, the system can be configured to electronicallytransmit the generated one or more surgical plans or otherwise allow auser to view or access the one or more surgical plans. For example, insome embodiments, a user access point system can be configured toreceive one or more surgical plans and/or steps thereof at block 222.The user access point system can be a personal computer (PC),smartphone, tablet PC, or any other electronic device used by one ormore medical personnel. The one or more generated plans can be sent toone or more medical personnel by e-mail, application message ornotification, or the like.

The user access point system can be configured to display the one ormore surgical plans and/or steps thereof at block 224. In someembodiments, a PDF or other viewable copy of the actual surgical plancan be transmitted to the user access point system for viewing. Forexample, FIG. 5 illustrates an example surgical plan in the form of aPDF memo that can be sent to medical personnel through one or moreelectronic communications methods, such as e-mail. In other embodiments,a link can be transmitted to a medical personnel or user access pointsystem. By activating the link, the user access point system can beconfigured to display the one or more generated surgical plans, forexample on a webpage or application page. For example, FIG. 6illustrates an example surgical plan on a system platform, such as awebpage or application page viewable to one or more medical personnel bythe system.

As briefly discussed above, a single surgical plan can comprise one ormore steps, such as a PSO, SPO, cage insertion, and/or screw insertion.Each step of the plan can relate to a particular surgical procedureand/or result starting from a pre-operative state. For example, asurgical plan can comprise one, two, three, four, five, six, seven,eight, nine, ten, or more steps. All steps of a surgical plan can bemade viewable to a user by the system. In some embodiments, the systemand/or user access point system can be configured to allow a user to“deactivate” one or more steps, in order for example. In otherembodiments, the system can be configured to allow a user to“deactivate” all steps of a plan at once to essentially view apre-operative state.

In some embodiments, the image analysis, case simulation, and/orsurgical planning can utilize one or more three-dimensional medicalimages to generate a three-dimensional plan. In certain embodiments, oneor more two-dimensional medical images can be combined and/or otherwiseutilized to generate a three-dimensional plan. For example, one or morex-ray images from different views, such as sagittal and/or frontalviews, can be combined to generate a three-dimensional composite image,which can in turn be utilized to generate a three-dimensional surgicalplan. More specifically, in some embodiments, a frontal x-ray image anda sagittal x-ray image can be combined using one or more scaling methodsdiscussed above to form a composite three-dimensional image afterscaling and/or coordination. In some embodiments, a generatedthree-dimensional plan can be used to determine one or more desiredparameters and/or variables for one or more patient-specific spinalimplants, such as spinal rod(s), cage(s), and/or screw(s) in threedimensions.

The systems, methods, and devices described herein can also be used todesign, select, and/or produce other patient-specific implants as well,such as cages. In particular, CT scanning and/or three-dimensionalprinting can be utilized for the design, selection, and/or production ofpatient-specific cages. One or more features or techniques relating tocomposite x-ray images described herein can also be used for developingpatient-specific cages. EOS x-ray imaging systems can also be used inconnection with any embodiment herein.

Referring back to FIG. 2, in some embodiments, the user access pointsystem can be configured to receive input from one or more medicalpersonnel at block 226 regarding the generated one or more plans. Forexample, one or more medical personnel may select and/or validate one ofa plurality of surgical plans developed by the system. Additionally,and/or alternatively, one or more medical personnel may request that aparticular surgical plan be modified, for example by inserting commentsand/or changing certain parameters and/or variables. The user accesspoint system can be configured to relay or transmit such input to themain server system, which can further be configured to modify the one ormore surgical plans at block 228 based on input received from the one ormore medical personnel. At block 230, the system can be configured topresent the final surgical plan(s) to one or more medical personnel,such as a surgeon, through a user access point system.

Subsequently, in certain embodiments, the system can be configured toproduce, modify, and/or select, for example from pre-existing inventory,one or more patient-specific spinal rods, cages, and/or screws forimplantation. For example, the system can be configured to produce,modify, and/or select one or more spinal rods that can be bent orotherwise desirable and/or specific to a particular patient prior tosurgery. The one or more spinal rods can be configured to be used inminimally invasive surgery (MIS). In certain embodiments, an addedbenefit can be that real-time x-ray during surgery can be minimized ornot used altogether to minimize radiation by the patient. Further, insome embodiments, the system can be configured to produce, modify,and/or select one or more cages and/or screws desirable and/or otherwisespecific to a particular patient prior to surgery.

FIG. 3 is a flowchart illustrating an example embodiment(s) of implantproduction, case support, and data collection for developingpatient-specific spinal treatments, operations, and procedures. In someembodiments, a computing system at an implant production and/orselection facility can be configured to access and/or receive a finalsurgical plan or a plurality thereof at block 302, for example via theInternet or a portable electronic storage medium. The implant productionfacility can be configured to produce, modify, and/or select one or moreparts for the surgical procedure at block 304. For example, the implantproduction facility can be configured to produce a spinal rod(s),cage(s), and/or screw(s) based on specifications and/or materialsspecified in the surgical plan(s). Similarly, the implant productionfacility can be configured to select and/or modify one or morepre-produced spinal rods, cages, and/or screws based on specificationsand/or materials specified in the one or more surgical plans.

In some embodiments, a spinal rod, cage, and/or screw can be producedfrom one or more different materials. The particular material to be usedfor a particular patient-specific rod(s), screw(s), and/or cage(s) candepend on data and/or can be selected by a surgeon. The particularmaterial can also depend on the particular patient's height, weight,age, bone density, and/or bone strength, among others.

FIGS. 7A and 7B illustrate example embodiments of spinal rods that canbe produced and/or selected, for example from a pre-existing range ofspinal rods, using certain embodiment(s) of systems, devices, andmethods for patient-specific spinal treatments, operations, andprocedures. In some embodiments, the system can be configured to design,select, and/or produce one or more of thoraco lumbar rods, cervicothoracic rods, MIS rods, and/or 3D bent rods. In certain embodiments, aspinal rod can be made of titanium, cobalt-chrome alloy, and/or anyother material.

As discussed above, in some embodiments, the system can be configured toproduce, select, and/or modify a rod that is bent in one or moredirections. Generally, it can be difficult, if not impossible, for asurgeon to bend a rod in even one direction, let alone more than onedirection, using tools prior to or during surgery. In contrast, byutilizing a composite of two-dimensional x-ray images and/orthree-dimensional medical images, the system can be configured toproduce, and/or select from pre-existing inventory, a rod that is bentor curved in more than one direction, for example sideways and also in asagittal direction.

FIG. 7C illustrates an example embodiment of a cage(s) that can beproduced and/or selected, for example from a pre-existing range ofcages, using certain embodiment(s) of systems, devices, and methods forpatient-specific spinal treatments, operations, and procedures. FIG. 7Dillustrates an example embodiment of a screw(s) that can be producedand/or selected, for example from a pre-existing range of screws, usingcertain embodiment(s) of systems, devices, and methods forpatient-specific spinal treatments, operations, and procedures.

Referring back to FIG. 3, one or more medical personnel can select oneor more implants, such as spinal rod(s), cage(s), and/or screw(s) forimplantation at block 306 that was produced, modified, and/or selectedby the implant production facility based on the surgical plan at block304.

In some embodiments, one or more medical personnel can attach and/oractivate one or more sensors for intraoperative tracking at block 308.The one or more sensors can be located in one or more screws and/or nutsfor attaching to a patient's vertebrae and/or tools for attaching thesame. One or more sensors that can be used in certain embodiments arediscussed in more detail below. In some embodiments, for spinalsurgeries, a sensor can be placed in or attached to every vertebra. Thiscan be advantageous for providing accurate data. However, this may notbe desirable in some situations due to the size of data. For example, alarge amount of unnecessary data can be collected, when the angle of thevertebrae can be one of the most important parameters. As such, a sensormay be attached to only a subset of vertebrae that can provide valuableposition and/or angular data of the spine.

In some embodiments, the system can be configured to utilize datacollected from one or more sensors inside or attached to one or morescrews implanted into the vertebrae instead of relying on imagingtechniques, for example assuming that an implanted screw will beparallel to an endplate, in order to provide intraoperative tracking. Inother words, angulation of a screw in a sagittal plane can be assumed tobe equal or substantially equal to the vertebra angulation. In someembodiments, a top portion of a screw can comprise an active or passivesensor. The top portion can be broken off later during surgery in someembodiments such that the sensors can be re-used. The one or more screwscomprising one or more sensors can be inserted into every vertebra or asubset thereof. For example, in some embodiments, sensors can beattached to all 20 vertebrae. In other embodiments, sensors can beattached to only a subset thereof, for example two or more sensorsattached to the upper lumbar and/or two or more attached to the lowervertebra. The sensors can then be utilized for providing data relatingto the position and/or angle or orientation of the vertebrae in sixdegrees of freedom (or nine degrees of freedom) in translation androtation in real-time, near real-time, and/or substantially real-time.The raw data collected by the one or more sensors can be transmitted toa computer system to translate the raw data into tracking the positionand/or orientation of one or more vertebrae, for example to assist indetermining a spinal curvature and/or surgical correction.

Based on real-time, near real-time, and/or substantially intraoperativetracking or monitoring, the system can be configured to track theposition and/or orientation or angulation of the vertebrae and/orscrew(s). In other words, in some embodiments, correction of the spineduring surgery can be monitored in real-time, near real-time, and/orsubstantially real-time. Referring again to FIG. 3, in certainembodiments, tracking data corresponding to the position and/orangulation of each vertebra can be transmitted to the main server systemand/or a client system at the medical facility at block 310.

In certain embodiments, after one or more medical personnel inserts one,two, or more screws into the spine of a patient, the main server systemand/or medical facility client system can be configured to track,analyze, and/or store movement of the different vertebrae during thecorrection and other operating procedure data at block 312. One or moremedical personnel can thus visualize or otherwise track the position,orientation, correction and/or angulation of the vertebrae in real-time,near real-time, and/or substantially real-time and determine whendesirable conditions, for example matching a pre-determined surgicalplan, have been obtained. Such live-tracking can provide substantialassistance to the medical personnel. For example, without intraoperativetracking, a surgeon may believe that a 30 degree correction can beobtained when PSS is performed; however, in reality, a performed PSS mayonly result in a 10 degree correction. By providing intraoperativetracking or monitoring, in such situations, the surgeon can make furthercorrections as necessary before closing up the operation.

In certain embodiments, the system can be configured to conduct analysisof the tracked data by comparing the same to a pre-determined surgicalplan. To do so, the system can retrieve data from the plan database 216and/or operation database 218. Based on such comparison and/or analysis,the system can be configured dynamically generate and/or provideguidance to the surgeon during the operation in real-time and/or nearreal-time in block 314. For example, based on the tracked data, thesystem can be configured to instruct or guide the surgeon to change theangle of one or more vertebra based on the tracked data to obtain acurvature of the spine closer to the pre-determined planned.

The system can further be configured to provide an audible and/orvisible alert and/or guidance to the surgeon. The audible and/or visiblealert and/or guidance can comprise instructions to the surgeon toperform the surgery in a particular way or degree and/or alert thesurgeon when the position and/or angulation of one or more vertebrae iswithin a predetermined threshold. For example, the system can beconfigured to provide an alert when the position and/or angulation ofone or more screws and/or vertebrae is within about 1%, about 2%, about3%, about 4%, about 5%, about 10%, about 15%, about 20%, about 25% ofthe predetermined plan and/or when within a range defined by two of theaforementioned values. In certain embodiments, the system can beconfigured to provide a visual depiction of the position, location,orientation, and/or angulation of each vertebra on a display based onthe tracked data to guide the surgeon.

Once an acceptable level of angulation of the vertebrae if obtained, thesurgeon can insert a spinal rod and/or tighten the screws to the rod andlock all parts for example to complete the surgery at block 316. Incertain embodiments, the surgeon can then remove and/or deactivate theone or more sensors at block 318.

The system can further be configured to collect and/or utilizepostoperative data in certain embodiments, for example to providepredictive modeling and/or other post-operation features or services.Moreover, in some embodiments, the system can be configured to take intoaccount a level of sophistication and/or preferences of a surgeon toprovide surgeon-specific recommendations for future cases. In certainembodiments, comparison and/or analysis of preoperative, intraoperative,and/or postoperative data and/or surgeon input can be used to determinea skill level and/or strategic preferences of a surgeon. The particularskill level of the surgeon and/or strategic preferences can be used todevelop subsequent surgical planning for that surgeon. In addition, insome embodiments, data relating to growth of the spine and/or othersubsequent developments, such as relating to curvature, can also beobtained from one or more postoperative x-ray images. Such long-termeffects can also be utilized in preparing subsequent planning.

In some embodiments, as part of predictive modeling and/or machinelearning, the system can be configured to analyze one or more differentplans that were developed for a particular case. For example, in someembodiments, a first generated plan can be based on the strategy and/orobjectives of a surgeon. A second generated plan for the same case canbe based on data from scientific literature. A third generated plan forthe same case can be based on historical data collected by the systemthrough performance of surgical procedures. As more data is collected,and as more feedback and input are given and received from surgeons,and/or as more scientific research is conducted, the one or moregenerated plans and/or particular features thereof for a single case mayconverge. Certain parameters that converge more so than others can beutilized more heavily by the system in subsequent planning stages.Further, in certain embodiments, the system can be configured to comparea given case to previous cases in the planning stage. For example, thesystem can be configured to parse one or more databases to find one ormore spines that match a given case and/or certain features thereof tomake certain recommendations and/or predictions for planning.

FIG. 4 is a flowchart illustrating an example embodiment(s) of datacollection, machine learning, and predictive modeling for developingpatient-specific spinal treatments, operations, and procedures. In someembodiments, the system can be configured to retrieve preoperative,intraoperative, and/or postoperative data at block 402 from a plandatabase 216 and/or operation database 218.

In particular, in some embodiments, the system can be configured tocollect one or more sets of data comprising one or more quantitativeparameters and/or one or more x-ray images or other medical images. Theone or more quantitative parameters can comprise, for each or one ormore vertebra, and for each or one or more of pre-operation, planningstage, and/or post-operation, pelvic tilt (PT), pelvic incidence (PT),sacral slope (SS), lumbar lordosis (LL), PI-LL, thoracic kyphosis (TK),T1 pelvic angle, and/or sagittal vertical axis (SVA). The one or morex-ray images can be from pre-operation, planning stage, post-operation,and/or one or more years post-operation. The data collected can be usedto build a database for future reference for data-driven and/orpartially data-driven planning purposes for example.

In some embodiments, post-operation data can be collected continuouslyand/or periodically. For example, post-operation data, one or moreparameters or variables thereof, and/or one or more medical images, suchas x-ray images, of a patient's spine can be collected at or prior tothree months post-operation, at or around six months post-operation, ator around one year post-operation, at or around two yearspost-operation, at or around three years post-operation, at or aroundfour years post-operation, at or around five years post-operation, at oraround ten years post-operation, at or around fifteen yearspost-operation, at or around twenty years post-operation, and/or betweenany of the aforementioned values and/or within a range defined by two ofthe aforementioned values.

In certain embodiments, the system can be further configured to retrievesurgeon inputted data and/or literature-based data at block 404. Thesurgeon-based data and/or literature-based data can be retrieved fromthe surgeon database 212 and the literature database 214 as describedabove.

In some embodiments, the system can be configured to analyze thepreoperative, intraoperative, and/or postoperative data at block 406and/or generate a report or other statistics at block 408. For example,in some embodiments, the system can be configured to compare one or morex-ray images from the planning stage, the operation stage, andpost-operation. In order to provide an accurate comparison, the systemcan be configured to calibrate one or more x-ray images taken fromdifferent points in time in a similar manner as described above inrelation to calibration of one or more x-ray images from differentviews. For example, a common point of reference, such as the centerpoint of the sacrum endplate, can be selected on each of the x-rayimages taken from different points in time. A length of a commonfeature, such as a plate length, can also be identified and used as abasis for scaling the x-ray images.

Based on calibrated x-ray images, the system can be configured togenerate a report comprising an overlay of the spine of a patientpre-operation, intra-operation, and/or post-operation. One or morepost-operative x-ray images may be provided, for example after 1 monthfrom surgery, 6 months from surgery, 1 year from surgery, 2 years fromsurgery, 3 years from surgery, 5 years from surgery, 10 years fromsurgery, or the like. The x-ray images taken from different points intime and/or overlay(s) thereof can provide a visual sense of how closelythe surgery was performed and/or how closely the surgical results wereto the surgical plan.

The generated report can further comprise one or more sacral parameters,such as SVA, PI, LL, PI-LL, PT, or the like. The generated report canalso comprise a percentage of achievement in comparison to the surgicalplan. Accordingly, by comparing pre-operative and post-operative data,data relating to strengths and/or weaknesses of results of the surgicalplan can be obtained. Further, by comparing preoperative andintraoperative data, data relating to strengths and/or weaknesses ofimplementation by a particular surgeon can also be obtained.

The system can be configured to transmit the generated report and/orstatistics to the surgeon who performed the surgery. For example, a useraccess point system of the surgeon can receive such generated reportand/or statistics at block 410. After reviewing, the surgeon may provideinput at block 412 through the user access point system. For example,the surgeon may provide input that certain procedures and/or selectionscould be improved. The surgeon may also provide and/or change generalpreferences based on the report. All such data can be stored in adatabase of the system for future reference. Such analysis can then beused for subsequent pre-operating planning and/or case simulation atblock 414.

In certain embodiments, the system can also be configured to utilizemachine learning techniques or processes. For example, the system can beconfigured to learn from previous plans and surgeries based onclassification of a patient's severity, age, weight, height, and/or anyother personal feature. The system can be configured to extract datafrom such machine learning database and/or process.

Cage/Screw Planning Overview

As discussed herein, in certain embodiments, the system can beconfigured to design, produce, modify, and/or provide guidance forselection of one or more patient-specific screws and/or cages to provideincreased effectiveness of spinal surgery and/or to control cost. Forexample, one or more patient-specific screws and/or cages can beselected from a pre-existing range or inventory of screws and/or cages.In particular, in some embodiments, the system can be configured todesign, produce, modify, and/or provide guidance for selection of one ormore screws and/or cages to be used for a specific patient at a specificanatomical site, such as a particular vertebra and/or intervertebralspace, based at least in part on analysis of one or more medical images.For example, the system can be configured to design a specific screw(s)for insertion into a specific vertebra and/or a specific cage(s) forinsertion into a specific intervertebral space. In some embodiments, oneor more processes and/or techniques described herein in relation to cageand/or screw planning, design, production, and/or selection can beutilized in combination and/or in conjunction with one or more otherprocesses and/or techniques relating to cage, screw, and/or spinal rodplanning, design, production, and/or selection and/or intraoperativetracking and/or predictive modeling, such as one or more such processesand techniques described herein. For example, in certain embodiments, acage and/or screw design and/or planning technique can be used inconjunction with a spinal rod design and/or planning technique to ensureand/or maximize compatibility between the system-generated cage, screw,and/or spinal rod design and/or one or more design parameters thereof.

In particular, in some embodiments, the system can be configured toprovide and/or recommend screw selection and/or one or more designparameters and/or dimensions thereof, including for example the diameterand/or length of one or more screws, as determined based in part onanalysis of one or more x-ray images, MRI slices, and/or other medicalimage. Similarly, in some embodiments, the system can be configured toprovide and/or recommend cage selection and/or one or more designparameters and/or dimensions thereof, including for example a footprint,posterior height, anterior height, width, length, and/or lordosis, asdetermined based in part on analysis of one or more x-ray images, MRIslices, and/or other medical image.

In certain embodiments, a surgical plan and/or image analysis outputgenerated by the system can comprise one or more of a length(s),diameter(s), dimension(s), and/or angulation of an implant, such as acage(s) and/or screw(s). More specifically, in some embodiments, thesystem can be configured to determine and/or generate a desired lengthand/or diameter of a screw, for example for insertion into a particularvertebra of a particular patient. In certain embodiments, the system canbe configured to determine and/or generate a desired design, shape,size, and/or angulation of a specific interbody device, such as a cage,for example for insertion into a particular intervertebral space of aparticular patient.

As such, in some embodiments, the system can be configured to designand/or determine one or more desired parameters of a patient-specificspinal cage(s) and/or screw(s) based on one or more specifications of agenerated surgical plan(s), in order to ensure optimal and/or desiredcorrespondence between one or more implants and the specific anatomy ofa given patient. Accordingly, in certain embodiments, this process canreduce related costs by decreasing the necessary inventory of screwsand/or cages that must be manufactured and/or kept in stock. Relatedcosts can even further be reduced due to reduced sterilization costs ofthe same. An additional advantage can relate to decreased surgery timeand simplification of surgical procedures, thereby increasing efficiencyof surgery. For example, in some embodiments, the system can beconfigured to determine and/or generate certain criteria and/ordimensions for possibly acceptable screws and/or cages for use in asurgery for a particular patient. Based on such determination, incertain embodiments, a personalized caddie comprising a fewer selectionof cages and/or screws, as determined prior to surgery, can be providedat the time of surgery.

In certain embodiments, the system can be configured to determine one ormore desired parameters for one or more patient-specific screws and/orcages for a particular vertebral segment of interest using one or moreprocesses or techniques described herein. For example, for one or morepatient-specific screws, the system can be configured to determine alength, diameter, material, and/or type of one or more screws that areoptimal and/or desired for a particular vertebra of a particularpatient. For one or more patient-specific cages, the system can beconfigured to determine posterior height, anterior height, globalheight, angulation, material, and/or type of one or more cages that areoptimal and/or desired for a particular intervertebral space(s) for aparticular vertebral segment of interest. Based on the determined one ormore desired parameters for one or more patient-specific cages and/orscrews, in certain embodiments, the system can be configured todetermine a range of acceptable cages and/or screws, for example frompre-existing inventory, for implantation and/or use in spinal surgeryfor a patient. For example, in some embodiments, a range of acceptablecages and/or screws can be defined by a range within about ±1% margin oferror, about ±2% margin of error, about ±3% margin of error, about ±4%margin of error, about ±5% margin of error, about ±10% margin of error,about ±15% margin of error, about ±20% margin of error, about ±25%margin of error, and/or about ±30% margin of error from the determinedone or more desired parameters for one or more patient cages and/orscrews. In certain embodiments, a range of acceptable cages and/orscrews can be defined by a range of margin of error between two of theaforementioned values.

FIG. 8 is a flowchart illustrating an example embodiment(s) of cageand/or screw design, production, modification, and/or selection. Asillustrated in FIG. 8, in some embodiments, a medical facility clientsystem is configured to access and/or obtain one or more two-dimensionaland/or three-dimensional medical images at block 802. The one or moremedical images can comprise one or more sagittal and/or frontal viewx-ray images, flexion and/or extension x-ray images, CT images, MRIimages, and/or medical images obtained using one or more other imagingmodalities. In certain embodiments, a main server system can beconfigured to receive one or more two-dimensional and/orthree-dimensional medical images from the medical facility client systemat block 804, for example in a similar manner as described in relationto FIG. 2. In particular, one or more features described in relation toFIG. 2 relating to receiving and/or accessing medical images can beapplicable to one or more process illustrated in FIG. 8 as well.

In some embodiments, the system can be configured to calibrate and/orscale one or more two-dimensional and/or three-dimensional medicalimages at block 806, for example to generate one or more compositemedical images at block 808, for example in a similar manner asdescribed in relation to FIG. 2. In particular, one or more featuresdescribed in relation to FIG. 2 relating to calibrating and scalingmedical image(s) and generating a composite image(s) thereof can beapplicable to one or more processes illustrated in FIG. 8 as well.

In certain embodiments, the system can be further configured to conductone or more analyses of the one or more medical images at block 810. Forexample, in some embodiments, the system can be configured to utilizeposterior longitudinal ligament (PLL)-based analysis, anteriorlongitudinal ligament (ALL)-based analysis, combined ALL and PLL-basedanalysis, and/or mid-plate-based analysis for planning and/or designingpatient-specific cages as will be described in more detail below. In anyembodiments described herein, the system can be configured to furtheradjust any resulting parameters of a patient-specific cage(s), such asposterior height, anterior height, global height, and/or angulation, toensure that the resulting spinal correction after implantation and/orsimulated implantation of the patient-specific cage(s) does notoverstretch the ALL and/or PLL and/or result in an ALL and/or PLL thatis longer compared to the same before surgery. As such, in certainembodiments, the system can be configured to utilize the pre-operativeALL and/or PLL length as a not-to-exceed threshold(s) in specifying oneor more design parameters for one or more patient-specific cages, suchas posterior height, anterior height, and/or angulation of the one ormore patient-specific cages, for example as a system check afterdetermining one or more parameters for one or more patients-specificcages. For example, the system can be configured to ensure that, for avertebral segment of interest, the resulting ALL and/or PLL length afterimplantation and/or simulated implantation of an intervertebral cage(s)does not exceed about 100% of the pre-operative ALL and/or PLL length,about 95% of the pre-operative ALL and/or PLL length, about 90% of thepre-operative ALL and/or PLL length, about 85% of the pre-operative ALLand/or PLL length, about 80% of the pre-operative ALL and/or PLL length,about 75% of the pre-operative ALL and/or PLL length, about 70% of thepre-operative ALL and/or PLL length, about 65% of the pre-operative ALLand/or PLL length, and/or about 60% of the pre-operative ALL and/or PLLlength. In certain embodiments, the system can be configured to ensurethat, for a vertebral segment of interest, the resulting ALL and/or PLLlength after implantation and/or simulated implantation of anintervertebral cage(s) does not exceed a percentage of the pre-operativeALL and/or PLL length that is between two of the aforementioned values.

In some embodiments, analysis of the one or more medical images forpurposes of cage and/or screw planning can also depend on a number offactors, such as parameters and/or variables identified by surgeons,surgeon preferences, spinal anatomical data, literature, and/or ahistorical surgery database of the system. Accordingly, as illustratedin FIG. 8, the system at block 810 can be configured to communicate withone or more of a surgeon database 212, literature database 214, plandatabase 216, and/or operation database 218 in analyzing the one or moremedical images. Each or all of the surgeon database 212, literaturedatabase 214, plan database 216, and/or operation database 218 can beconfigured to be updated continuously and/or periodically.

In certain embodiments, based at least in part on the analyses conductedin block 810, the system can be configured to generate one or moredesired parameters for a patient-specific cage(s) and/or screw(s) atblock 812. For example, in some embodiments, the system can beconfigured to specify one or more of the following patient-specificparameters for one or more screws to be used in spinal surgery: root,length, and/or diameter. In addition, in certain embodiments, the systemcan be configured to specify one or more of the followingpatient-specific parameters for one or more cages to be implanted: type,design, shape, size, material, width, height, angulation, orientation,and/or length.

In some embodiments, a computing system at an implant productionfacility and/or medical facility client system can be configured toaccess and/or receive the generated desired one or more parameters for apatient-specific cage(s) and/or screw(s), for example via the Internetor a portable electronic storage medium. In certain embodiments, theimplant production and/or selection facility and/or medical facilityclient system can be configured to produce, modify, and/or provideguidance for selection of one or more patient-specific screws and/orcages at block 814, based at least in part on the generated one or moredesired parameters thereof.

Cage Planning—Posterior Approach

In certain embodiments, the system can be configured to provide cageplanning and/or cage design, as part of case support for example. Thesystem can be configured to determine certain cage(s) that are likely tofit a certain patient and recommend such one or more cage(s). Morespecifically, in some embodiments, the system can be configured todetermine and/or focus on the length(s) of one or more ligaments, suchas a posterior longitudinal ligament (PLL) and/or an anteriorlongitudinal ligament (ALL), that can be used as a guideline to ensurenot to over-distract a particular patient's spine and/or attempt toover-correct the spine beyond its physiological capability. Morespecifically, for cage selection, it can be important not to select acage(s) that may result in overstretching the spine beyond the length ofa particular patient's longitudinal ligament(s). Accordingly, in someembodiments, the system is configured to measure one or more dimensionsof one or more longitudinal ligaments, rather than simply measuring astraight line along the spinal column.

FIG. 9A is a flowchart illustrating an example embodiment(s) of cagedesign, production, modification, and/or selection. As illustrated inFIG. 9A, in some embodiments, the system can be configured to utilize aposterior approach or a posterior-anterior approach. More specifically,in certain embodiments, the system is configured to access and/or obtainone or more two-dimensional and/or three-dimensional medical images, forexample of a patient's spine, at block 902. The one or more medicalimages can comprise one or more sagittal and/or frontal view x-rayimages, flexion and/or extension x-ray images, CT images, MRI images,and/or medical images obtained using one or more other imagingmodalities.

In some embodiments, the system can be configured to measure the lengthof one or more ligaments from the one or more medical images. In orderto account for maximum length, in some embodiments, the system isconfigured to determine an extended ligament length, for example fromone or more medical images taken from when the patient was in anextension state. In certain embodiments, the system can be configured tomeasure the anterior and/or posterior length of a ligament accountingfor both the horizontal and vertical lengths.

In certain embodiments, the system can be configured to utilize only ameasurement of an anterior longitudinal ligament (ALL) in designing oneor more patient-specific cages. For example, an ALL length for avertebral segment of interest can be used as a maximum threshold for aresulting anterior curve (AC) after implanting one or moreintervertebral cages in the vertebral segment of interest. In someembodiments, in contrast to an ALL curve that follows the anteriorlongitudinal ligament, AC can refer to a curve that follows an anteriorboundary of the vertebral column and/or that goes through one or moreanterior corners of one or more vertebrae. In certain embodiments, ACmay not comprise rigid angles, for example may not be elbow-shaped, asillustrated in the example x-ray image 922 of FIG. 9C. Morespecifically, the system can be configured to ensure that, for avertebral segment of interest, the resulting AC length afterimplantation and/or simulated implantation of an intervertebral cagedoes not exceed about 100% of the ALL length, about 95% of the ALLlength, about 90% of the ALL length, about 85% of the ALL length, about80% of the ALL length, about 75% of the ALL length, use, about 70% ofthe ALL length, about 65% of the ALL length, and/or about 60% of the ALLlength. In other words, one or more of the above-identified values canbe used by the system as a not-to-exceed threshold for correcting thespine of a patient, for example as a system check after determining oneor more parameters for one or more patients-specific cages. In certainembodiments, the system can be configured to ensure that, for avertebral segment of interest, the resulting AC length afterimplantation and/or simulated implantation of an intervertebral cagedoes not exceed a percentage of the ALL length between two of theaforementioned values.

In certain embodiments, the system can be configured to utilize only ameasurement of posterior longitudinal ligament (PLL) in designing one ormore patient-specific cages. For example, a PLL length for a vertebralsegment of interest can be used as a maximum threshold for a resultingposterior curve (PC) after implanting one or more intervertebral cagesin the vertebral segment of interest. In some embodiments, in contrastto a PLL curve that follows the posterior longitudinal ligament, PC canrefer to a curve that follows a posterior boundary of the vertebralcolumn and/or that goes through one or more posterior corners of one ormore vertebrae. In certain embodiments, PC may not comprise rigidangles, for example may not be elbow-shaped, as illustrated in theexample x-ray image 922 of FIG. 9C. More specifically, the system can beconfigured to ensure that, for a vertebral segment of interest, theresulting PC length after implantation and/or simulated implantation ofan intervertebral cage does not exceed about 100% of the PLL length,about 95% of the PLL length, about 90% of the PLL length, about 85% ofthe PLL length, about 80% of the PLL length, about 75% of the PLLlength, use, about 70% of the PLL length, about 65% of the PLL length,and/or about 60% of the PLL length. In other words, one or more of theabove-identified values can be used by the system as a not-to-exceedthreshold for correcting the spine of a patient, for example as a systemcheck after determining one or more parameters for one or morepatients-specific cages. In certain embodiments, the system can beconfigured to ensure that, for a vertebral segment of interest, theresulting PC length after implantation and/or simulated implantation ofan intervertebral cage does not exceed a percentage of the PLL lengthbetween two of the aforementioned values.

In certain embodiments, the system can be configured to utilizemeasurements of both the posterior longitudinal ligament (PLL) and theanterior longitudinal ligament (ALL) in designing one or morepatient-specific cages. For example, PLL and ALL lengths for a vertebralsegment of interest can be used as a maximum threshold(s) for aresulting posterior curve (PC) and anterior curve (AC) after implantingone or more intervertebral cages in the vertebral segment of interest.More specifically, the system can be configured to ensure that, for avertebral segment of interest, the resulting PC and/or AC length afterimplantation and/or simulated implantation of an intervertebral cagedoes not exceed about 100% of the PLL and/or ALL length, about 95% ofthe PLL and/or ALL length, about 90% of the PLL and/or ALL length, about85% of the PLL and/or ALL length, about 80% of the PLL and/or ALLlength, about 75% of the PLL and/or ALL length, use, about 70% of thePLL and/or ALL length, about 65% of the PLL and/or ALL length, and/orabout 60% of the PLL and/or ALL length. In other words, one or more ofthe above-identified values can be used by the system as a not-to-exceedthreshold for correcting the spine of a patient, for example as a systemcheck after determining one or more parameters for one or morepatients-specific cages. In certain embodiments, the system can beconfigured to ensure that, for a vertebral segment of interest, theresulting PC and/or AC length after implantation and/or simulatedimplantation of an intervertebral cage does not exceed a percentage ofthe PLL and/or ALL length between two of the aforementioned values.

More specifically, in certain embodiments, the system can be configuredto determine lengths of one or more ligaments of a patient, such as theposterior longitudinal ligament (PLL) and/or anterior longitudinalligament (ALL), from one or more MRI images and/or x-ray images. The oneor more x-ray images can be dynamic, such as flexion and/or extensionx-rays. In the illustrated embodiment in FIG. 9A, the system can beconfigured to determine the length of a patient's PLL and/or ALL of aparticular vertebral segment on one or more MRI, x-ray and/or dynamicx-ray images at block 904, although this process may be optional incertain embodiments.

In certain embodiments, from one or more MRI images, the system can beconfigured to obtain the length(s) of one or more ligaments, such as ALLand/or PLL, through direct measurement of the ligaments. FIG. 9Billustrates an example embodiment(s) of cage design, production,modification, and/or selection. In particular, in the example embodimentillustrated in FIG. 9B, an MRI image 920 is provided on which the systemis configured to determine lengths of both the ALL and PLL. In theillustrated example, the length of the PLL of a particular vertebralsegment is determined to be roughly 61.5 mm, and the length of an ALL ofthe same particular vertebral segment is determined to be roughly 64.3mm.

Further, in some embodiments, from one or more x-ray images and/ordynamic x-ray images, the system can be configured to estimate and/ordetermine the length(s) of one or more ligaments, such as ALL and/orPLL, such as through estimating a range of motion of one vertebraagainst another considering one intervertebral space. For example, froman x-ray image taken when a patient is in a flexed position, the systemcan be configured to estimate and/or determine a PLL length for aparticular vertebral segment of the patient.

As such, referring back to FIG. 9A, in certain embodiments, the systemcan be configured to determine and/or estimate the length of aparticular patient's PLL along a particular vertebral segment on aflexion x-ray image at block 906. FIG. 9B further illustrates a flexionx-ray image 916, on which the system is configured to estimate and/ordetermine the length of the PLL along a particular vertebral segment. Inthe illustrated example, the length of the PLL along a particularvertebral segment is determined to be roughly 61.5 mm.

Referring back to FIG. 9A, in some embodiments, the system can beconfigured to determine and/or estimate the length of a particularpatient's ALL along a particular vertebral segment on an extension x-rayimage at block 908. FIG. 9B further also illustrates an extension x-rayimage 918, on which the system is configured to estimate and/ordetermine the length of the ALL along a particular vertebral segment. Inthe illustrated example, the length of the ALL along a particularvertebral segment is determined to be roughly 64.3 mm.

Based at least in part on such measurements taken from one or more MRIimages and/or x-ray or dynamic x-ray images, the system can further beconfigured to determine and/or take into account the anterior andposterior height of a cage(s), and further take into account angulationand/or positioning in certain embodiments, in designing and/ordetermining one or more patient-specific cages. As such, in certainembodiments, by taking into account an assessment of the length(s) ofone or more ligaments, the system can ensure that a generate surgicalplan does not over-distract a patient's spine.

More specifically, referring back to FIG. 9A, the system can beconfigured to overlay, draw, and/or allow a user to overlay and/or drawone or more anterior curves (AC) along the anterior column and/or one ormore posterior curves (PC) along the posterior column on an x-ray image,such as a postural x-ray image, at block 910 for further analysis. Incertain embodiments, the system can be configured to identify one ormore curves along the AC and/or PC automatically and/orsemi-automatically, for example without displaying and/or overlaying thesame on a display.

FIG. 9C illustrates an example embodiment(s) of cage design, production,modification, and/or selection. In particular, in the example embodimentillustrated in FIG. 9C, a postural x-ray image 922 is provided on whichthe system is configured to simulate implanting a cage(s) withparticular dimensions to a vertebral segment. In the illustrated examplein particular, the system can be configured to overlay a posterior curveand/or an anterior curve. In the illustrated example, the PC isidentified and/or drawn as going through the 4 posterior vertebracorner, and the AC is identified and/or drawn as going through the 4anterior vertebra corner. In the illustrated example, the AC for aparticular vertebral segment of interest is determined to be roughly59.4 mm, and the PC for the particular vertebral segment of interest isdetermined to be roughly 56.5 mm.

Referring back to FIG. 9A, in some embodiments, the system can beconfigured to increase the posterior height (Hpost) of one or more cagesuntil the length of PC equals the length of PLL on an x-ray image, suchas a postural x-ray image, at block 912 for further analysis. As such,at block 912, in certain embodiments, the system can be configured tomodify the postural x-ray image on which the AC and/or PC was drawn byincreasing Hpost of a proposed cage for implantation until the length ofthe PC equals the length of the PLL. In certain embodiments, the systemcan be configured to increase Hpost of one or more cages until thelength of PC equals the length of PLL automatically and/orsemi-automatically, for example without displaying and/or overlaying thesame on a display. In some embodiments, the system can be configured toincrease Hpost of one or more cages while ensuring that the PC isshorter than or equal to or substantially equal to the PLL.

FIG. 9C further illustrates a postural x-ray image 924 on which thesystem is configured to increase Hpost of a proposed cage in aparticular intervertebral space for implantation until the length of PCbecomes equal to the length of PLL. In the illustrated example, theHpost of a proposed cage is increased until the PC curve length and PLLlength are both determined and/or estimated to be roughly 61.5 mm.Further, in the illustrated example, the length of the AC is determinedto be roughly 62.1 mm, and the length of the ALL is determined to beroughly 64.3 mm.

Referring back to FIG. 9A, in some embodiments, the system can beconfigured to increase lordosis, while keeping the AC length shorterthan or at least equal to the ALL length for a particular vertebralsegment of interest at block 914. As such, at block 914, in certainembodiments, the system can be configured to further modify the posturalx-ray image on which the AC and/or PC was drawn and/or on which Hpost ofa proposed cage was increased, for example by increasing lordosis. Incertain embodiments, the system can be configured to increase lordosis,while keeping the AC length shorter than or at least equal to the ALLlength automatically and/or semi-automatically, for example withoutdisplaying and/or overlaying the same on a display.

FIG. 9C further illustrates a postural x-ray image 926 on which thesystem is configured to increase lordosis while keeping the AC lengthshorter than or at least equal to the ALL length for a particularvertebral segment of interest. In the illustrated example, the PC lengthand PLL length are both determined and/or estimated to be roughly 61.5mm, as previously determined in the postural x-ray image 924. Further,in the illustrated example, by increasing lordosis in the mannerdescribed above, the AC length is determined to be roughly 64 mm, andthe length of the ALL is determined to be roughly 64.3 mm. Based atleast in part on the modified and/or identified PC, PLL, AC, and/or ALL,the system can be configured to determine, design, and/or estimate oneor more dimensions of a patient-specific cage, such as anterior height(Hant), posterior height (Hpost), and/or lordosis, for example tomaximize spinal correction results without overstretching the patient'sspine. For example, in the illustrated example, it can be determinedand/or estimated that a patient-specific cage should comprise a Hant ofroughly 10 mm, Hpost of roughly 7 mm, and/or lordosis of about 6°.

As such, in some embodiments, when designing and/or planningpatient-specific cage(s), the system can be configured to determine,estimate, and/or define the anterior and/or posterior height of one ormore patient-specific cages by assessing and/or analyzing the length(s)of one or more spinal ligaments, such as ALL and/or PLL, in order to notover-distract the spine. Further, in certain embodiments, the system canalso be configured to determine, estimate, and/or define angulation ofone or more patient-specific cages by taking into account positioning ofsuch one or more cages, for example on a postural x-ray or other medicalimage. In some embodiments, such determined one or more cage parameters,such as posterior height, anterior height, global height, length, width,angulation, or the like, can be used by the system to design, produce,and/or select, for example from a pre-existing range or inventory, oneor more patient-specific cages.

In some embodiments, the system can be configured to utilize amathematical approach in calculating and/or estimating a desiredposterior height, anterior height, global height, and/or angulation ofone or more patient-specific cages, without utilizing a trial-and-errortype approach. For example, in certain embodiments, the system can beconfigured to calculate and/or estimate a posterior height of one ormore patient-specific cages by subtracting a length of a PC from alength of a PLL of a particular vertebral segment of interest, such as asegment that includes two vertebrae and an intervertebral space inbetween. Similarly, in some embodiments, the system can be configured tocalculate and/or estimate an anterior height of one or morepatient-specific cages by subtracting a length of an AC from a length ofan ALL of a particular vertebral segment of interest, such as a segmentthat includes two vertebrae and an intervertebral space in between.Further, in some embodiments, the system can be configured to take anaverage of the difference between PC and PLL for a particular vertebralsegment of interest and the difference between AC and ALL for theparticular vertebral segment to calculate and/or estimate a height orglobal height of one or more patient-specific cages.

Cage Planning—Anterior Approach

In some embodiments, the system can be configured to utilize an anterioror posterior approach in measuring one or more ligament lengths andusing the same in designing or determining one or more dimensions of apatient-specific cage to ensure that the patient spine is notoverstretched as a result of surgery. FIG. 10A is a flowchartillustrating an example embodiment(s) of cage design, production,modification, and/or selection. In particular, as illustrated in FIG.10A, in some embodiments, the system can be configured to utilize ananterior approach.

More specifically, in certain embodiments, the system is configured toaccess and/or obtain one or more two-dimensional and/orthree-dimensional medical images, for example of a patient's spine, atblock 1002. The one or more medical images can comprise one or moresagittal and/or frontal view x-ray images, flexion and/or extensionx-ray images, CT images, MRI images, and/or medical images obtainedusing one or more other imaging modalities.

In some embodiments, the system can be configured to determine lengthsof one or more ligaments of a patient, such as the posteriorlongitudinal ligament (PLL) and/or anterior longitudinal ligament (ALL),from one or more MRI images and/or x-ray images. The one or more x-rayimages can be dynamic, such as flexion and/or extension x-rays. In theillustrated embodiment in FIG. 10A, the system can be configured todetermine the length of a patient's PLL for a particular vertebralsegment on one or more MRI, x-ray and/or dynamic x-ray images at block1004, although this process may be optional in certain embodiments.

In certain embodiments, from one or more MRI images, the system can beconfigured to obtain the length(s) of one or more ligaments, such as ALLand/or PLL, through direct measurement of the ligaments. FIG. 10Billustrates an example embodiment(s) of cage design, production,modification, and/or selection. In particular, in the example embodimentillustrated in FIG. 10B, an MRI image 1016 is provided on which thesystem is configured to determine the length of the PLL for a vertebralsegment of interest. In the illustrated example, the length of the PLLof a particular vertebral segment is determined to be roughly 61.5 mm.

In addition, in some embodiments, from one or more x-ray images and/ordynamic x-ray images, the system can be configured to estimate and/ordetermine the length(s) of one or more ligaments, such as ALL and/orPLL, such as through estimating a range of motion of one vertebraagainst another considering intervertebral spacing. For example, from aflexion x-ray image, the system can be configured to estimate and/ordetermine a PLL length for a particular vertebral segment of thepatient.

As such, referring back to FIG. 10A, in certain embodiments, the systemcan be configured to determine and/or estimate the length of aparticular patient's PLL along a particular vertebral segment on aflexion x-ray image at block 1006. FIG. 10B further illustrates aflexion x-ray image 1014, on which the system is configured to estimateand/or determine the length of the PLL along a particular vertebralsegment. In the illustrated example, the length of the PLL along aparticular vertebral segment is determined to be roughly 61.5 mm.

In some embodiments, the system can be configured to overlay, draw,and/or allow a user to overlay and/or draw one or more anterior curves(AC) along the anterior column and/or one or more posterior curves (PC)along the posterior column (PC) on an x-ray image, such as a posturalx-ray image. Referring back to FIG. 10A, in certain embodiments, thesystem can be configured to overlay, draw, and/or allow a user tooverlay and/or draw one or more posterior curves for a particularvertebral segment of interest at block 1008. In certain embodiments, thesystem can be configured to identify one or more posterior curves and/oranterior curves automatically and/or semi-automatically, for examplewithout displaying and/or overlaying the same on a display.

FIG. 10C illustrates an example embodiment(s) of cage design,production, modification, and/or selection. In particular, in theexample embodiment illustrated in FIG. 10C, a postural x-ray image 922is provided on which the system is configured to simulate implanting acage(s) with particular dimensions to a vertebral segment. In theillustrated example in particular, the system can be configured tooverlay a PC. In the illustrated example, the posterior curve isidentified and/or drawn as going through the 4 posterior vertebracorner. In the illustrated example, the posterior curve for a particularvertebral segment of interest is determined to be roughly 56.5 mm.

Referring back to FIG. 10A, in some embodiments, the system can beconfigured to apply and/or increase lordosis of a cage(s) at block 1010.As such, at block 1010, in certain embodiments, the system can beconfigured to further modify the postural x-ray image on which theposterior curve was drawn, for example by applying and/or increasinglordosis of a simulated cage. In certain embodiments, the system can beconfigured to apply and/or increase lordosis of the cage automaticallyand/or semi-automatically, for example without displaying and/oroverlaying the same on a display.

FIG. 10C further illustrates a postural x-ray image 1020 on which thesystem is configured to apply and/or increase lordosis of a cage(s). Inthe illustrated example, by applying lordosis of a cage for a particularintervertebral space, it can be determined that the cage lordosis isroughly 9° and that the PC for a vertebral segment of interest isroughly 54.7 mm.

Referring back to FIG. 10A, in some embodiments, the system can beconfigured to increase the cage height, for example globally, until PCequals PLL on an x-ray image, such as a postural x-ray image, at block1012. As such, at block 1012, in certain embodiments, the system can beconfigured to modify a postural x-ray image on which a PC curve wasdrawn and/or cage lordosis was applied, by increasing global cage heightfor a particular intervertebral space until the length of the PC curveequals the length of PLL for a particular vertebral segment. In certainembodiments, the system can be configured to increase global cage heightuntil PC equals PLL automatically and/or semi-automatically, for examplewithout displaying and/or overlaying the same on a display. In someembodiments, the system can be configured to increase global cage heightwhile ensuring that the PC is shorter than or equal to or substantiallyequal to the PLL.

FIG. 10C further illustrates a postural x-ray image 1022 on which thesystem is configured to increase global height for a cage(s) in aparticular intervertebral space(s) until PC becomes equal to PLL. In theillustrated example, Hant of the proposed cage is determined to beroughly 12 mm, and Hpost of the proposed cage is determined to beroughly 8 mm, after globally increasing the cage height until the PClength equals the PLL length for the particular vertebral segment ofinterest. Further, in the illustrated example, lordosis is stillmaintained at 9°, and the lengths of the PC and PLL are determined to beroughly 61.5 mm after globally increasing the cage height accordingly.

As such, in some embodiments, when designing and/or planningpatient-specific cage(s), the system can be configured to determine,estimate, and/or define the anterior and/or posterior height of one ormore patient-specific cages by assessing and/or analyzing the length ofa single spinal ligament, such as either ALL or PLL, in order to notover-distract the spine. Further, in certain embodiments, the system canalso be configured to determine, estimate, and/or define angulation ofone or more patient-specific cages by taking into account positioning ofsuch one or more cages, for example on a postural x-ray or other medicalimage. In some embodiments, such determined one or more cage parameters,such as anterior height, posterior height, global height, length, widthangulation, or the like, can be used by the system to design, produce,and/or select, for example from a pre-existing range or inventory, oneor more patient-specific cages.

Cage Planning—Mid-Plate Analysis

In some embodiments, the system can be configured to analyze and/orother focus on a curve connecting the middle of one or more vertebralendplates for designing or determining one or more dimensions of apatient-specific cage, for example to ensure that the patient spine isnot overstretched or over-distracted as a result of surgery. FIG. 11A isa flowchart illustrating an example embodiment(s) of cage design,production, modification, and/or selection. FIG. 11B is a schematicdiagram illustrating an example embodiment(s) of cage design,production, modification, and/or selection. In particular, asillustrated in FIGS. 11A and 11B, in some embodiments, the system can beconfigured to utilize a mid-plate approach.

More specifically, in certain embodiments, the system can be configuredto measure the height of one or more discs along a curve crossing themiddle of the vertebrae endplates, for example taking into account bothvertical and/or horizontal displacement. Based on the determined one ormore disc height measurements, the system can be configured to calculatethe mean repartition of each of the disc heights as a percentage of thetotal disc height, such as along the spine or portion of the spine, forexample lumbar spine as illustrated in the example embodiment of FIG.11B. In some embodiments, the system can be configured to obtain theposition and/or undulation of one or more mid-plate positions along thecurve. Such data can be compared to historical data from previous casesand/or to data from scientific literature for predictive planningpurposes, such as to design and/or determine one or more dimensions,such as global height and/or angulation, of one or more patient-specificcages.

In some embodiments, the system can be configured to utilize adata-driven technique and/or process for cage planning. In particular,in certain embodiments, the system can be further configured to compareand/or analyze such percentage(s) against data obtained and/orpre-existing from a healthy and/or asymptomatic population. Inparticular, in certain embodiments, if the calculated percentage of aparticular disc height for a particular patient is different compared tothat of the healthy population, the system can be configured todetermine that a cage(s) needs to be implanted for that disc(s). Basedon such analysis, the system can further be configured to determine oneor more dimensions for a patient-specific cage(s) for that disc(s) torestore an acceptable disc height, measured as a percentage of the totaldisc height for example. In addition, in certain embodiments, the sameand/or similar technique or process can also be applied to definerepartition of angulations among cages to obtain a defined global anglecorrection.

Referring back to FIG. 11A, in some embodiments, the system can beconfigured to access spinal measurement data of a healthy orasymptomatic population at block 1102. In some embodiments, the systemcan comprise a database 1104 that has stored data collected from ahealthy and/or asymptomatic population, including data related to theheight and/or angulation of one or more particular discs as a percentageof total disc height and/or as a percentage of total angulation. Suchdatabase 1104 can be internal or external to the system. Such database1104 may be built by the system or may be imported into and/or otherwiseaccessed by the system.

In certain embodiments, the database 1104 can be updated continuouslyand/or periodically. In particular, in some embodiments, the system canbe configured to determine and/or update repartition of individual discheight(s) and/or angulation(s) as a percentage of total disc heightand/or angulation for a healthy population at block 1106, for example asnew and/or updated healthy population spinal data is made available.This process can be repeated in some embodiments continuously and/orperiodically in order to update the database 1104.

More specifically, in order to build a reference database of a healthyand/or asymptomatic population, the system can be configured to measureand/or otherwise determined individual disc height along a straight lineor curve crossing the middle of the vertebrae. In addition and/oralternatively, in certain embodiments, the system can be configured tomeasure the disc height along an anterior and/or posterior line. In someembodiments, the system can be configured to automatically measure ordetermine the disc height(s) based on a medical image(s). In otherembodiments, the disc height(s) are determined manually.

Based on the determined disc height and/or angulation measurement(s),the system can be configured to determine the mean repartition of alldisc heights and/or angulation as a percentage(s) of the total discheight and/or angulation. By doing so, the system can be configured toobtain reference percentage(s) of repartition of disc heights for ahealthy population, as illustrated in schematic 1120 of FIG. 11B. Inaddition, the same and/or similar technique or process can also beapplied to define repartition of angulations among cages to obtain adefined global angle correction.

This reference data 1120 can subsequently be used for comparison withspecific patient data to determine recommended cage(s) for that patient.In particular, referring back to FIG. 11A, the system can be configuredto analyze one or more detail images of a patient, such as x-ray, MRI,CT, or the like, to determine repartition of one or more disc heightsand/or angulations as a percentage of the patient's total disc heightand/or angulation at block 1108.

In order to do so, referring to schematic 1116 of FIG. 11B, the systemcan be configured to determine the height of one or more individualdiscs of a patient along a mid-plate curve, for example based on one ormore medical images of a vertebral segment of interest of the patient.In some embodiments, one or more disc heights of a particular patientcan be measured or obtained pre-operation. In certain embodiments, thesystem can be configured to obtain measurement of a patient's lumbardisc height, automatically or manually from one or more medical images,by level and/or total. Based on such measurement, the system can beconfigured to determine that a particular patient may need a cagereplacement for one or more discs for example if there is a largediscrepancy compared to data from a healthy population. In theillustrated embodiment 1116, the system may determine that a patient mayneed cage replacements at L4L5 and/or L5S1.

As discussed, in certain embodiments, the system can be configured touse the measured or determined individual disc heights and/orangulations to calculate percentage(s) of each disc height and/orangulation compared to the total sum of disc heights and/or angulationsat block 1108. For example, in the example embodiment illustrated inFIG. 11B, and in particular in schematic 1118, the height of L4L5 andL5S1 as a percentage(s) of the total lumbar spine disc height can bedetermined to be roughly 9.5% and 19.0%.

In some embodiments, the system can further be configured to compareand/or analyze such percentage(s) against the data obtained from ahealthy population at block 1110. For example, in the exampleillustrated in FIG. 11B and in particular in schematic 1120, the discheight of L4L5 and L5S1 as a percentage(s) of the total lumbar spinedisc height can correspond to 20.7% and 27.6% for a healthy population.

If the calculated percentage of a particular disc height for aparticular patient is too low compared to the healthy population, thesystem can be configured to determine that cage(s) must be implanted forthat disc to restore an acceptable disc height. In certain embodiments,if a particular disc height of a particular patient is below apredetermined threshold value when compared to the healthy population,the system can be configured to recommend cage implantation of a certainheight or range of heights. For example, in some embodiments, the systemcan be configured propose one or more cage heights, such as anteriorand/or posterior cage heights, for replacing a particular disc at block1112 of FIG. 11A.

In some embodiments, the system can be further configured to simulateresults of one or more proposed cage heights and/or angulations and/orcompare the simulated results in terms of percentage of one or more cageheights and/or angulations against data collected from a healthypopulation at block 1114. Based on such simulation results, the systemcan be configured to generate modified proposed height(s),angulation(s), and/or other dimensions for one or more cages. As such,in some embodiments, the system can further be configured to compareand/or analyze such percentages against the database of an asymptomaticpopulation. Based on such comparison, if the difference between suchvalues is below a predetermined threshold, the system can be configuredto accept or finalize the cage planning proposal. However, if thedifference is above a certain predetermined threshold, the system can beconfigured to reject the cage planning proposal and continue to refinethe proposal, for example by increasing and/or decreasing one or moreheights, angulations, or other dimensions of a cage. As such, thesystem, in some embodiments, can be configured to apply an iterativeprocess or technique. In some embodiments, such determined one or morecage parameters, such as posterior height, anterior height, globalheight, length, width, angulation, or the like, can be used by thesystem to design, produce, and/or select, for example from apre-existing range or inventory, one or more patient-specific cages.

Screw Planning

As discussed herein, in certain embodiments, the system can beconfigured to design, produce, modify, and/or provide guidance forselection of one or more patient-specific screws. This can beadvantageous by substantially reducing costs by decreasing the inventoryof screws that need to be manufactured and/or kept in stock. Relatedcosts can even further be reduced due to reduced sterilization costs. Anadditional advantage can be decreased surgery time and simplification ofsurgical procedures for the surgeon and staff as fewer screws, cages,and/or sets thereof are provided as a personalized caddie for eachsurgery, thereby increasing efficiency of the surgery.

For example, as opposed to providing every single available screw orsets of screws, which can amount to 400 or 500 or more screws for atypical deformity tray, some embodiments can allow provision of onlyabout 10 screws, about 20 screws, about 30 screws, about 40 screws,about 50 screws, about 60 screws, about 70 screws, about 80 screws,about 90 screws, about 100 screws, about 110 screws, about 120 screws,about 130 screws, about 140 screws, about 150 screws, about 160 screws,about 170 screws, about 180 screws, about 190 screws, about 200 screws,and/or a number of screws within a range defined by two of theaforementioned values for an operation that generally requires onlyabout 2 to 60 screws for example. As a non-limiting example, in someembodiments, for a 5 level surgery, the system can be configured toprovide selection and/or recommendation to include a maximum of 72screws with particular dimensions in a personalized caddie.

As such, in some embodiments, the system is configured to assess one ormore adequate dimensions to design patient-specific screws and/or otherimplants from analysis of and/or measurements obtained from one or moremedical images, such as two-dimensional x-ray images and/or MRI sagittalslice(s). In certain embodiments, the system can be configured tocombine one or more measurements obtained from one or more medicalimages with literature and/or data driven additions to obtain sufficientaccuracy and/or precision to substantially define patient-specific screwdesigns and/or one or more dimensions thereof.

FIG. 12A is a flowchart illustrating an example embodiment(s) of screwdesign, production, modification, and/or selection. In particular, asillustrated in FIG. 12A, the system, in some embodiments, can beconfigured to design and/or determine one or more dimensions of apatient-specific screw(s) for insertion into a particular vertebra,which can be used to design, produce, modify, and/or select one or morepatient-specific screws.

In certain embodiments, the system is configured to access and/or obtainone or more two-dimensional and/or three-dimensional medical images, forexample of a patient's spine, at block 1202. The one or more medicalimages can comprise one or more sagittal and/or frontal view x-rayimages, flexion and/or extension x-ray images, CT images, MRI images,and/or medical images obtained using one or more other imagingmodalities. As such, in some embodiments, screw selection and/or designcan be based on three-dimensional imaging scan data, such as a CT scan.Based on a CT scan, the system can be configured slice a vertebra towhich a screw will be inserted and determine precise measurements ofeach required screw, such as the length and/or diameter of the screw.However, to do so, three-dimensional multi-planar reconstruction (MPR)may be required, which can be time-consuming. Also, a CT scan can benecessary, which is not a routine image.

In some embodiments, patient-specific screw design, selection, and/orrecommendation can be based on three-dimensional medical images and/ortwo-dimensional medical images, as described above in relation to FIG.2. For example, in some embodiments, the system can be configured toutilize one or more sagittal x-ray images only to obtain certainmeasurements. In certain embodiments, the system can be configured toutilize a sagittal x-ray image and a frontal x-ray image to obtain acomposite three-dimensional image as described above to obtain moreaccurate screw length and/or diameter estimates. In some embodiments,the system can be configured to utilize a full three-dimensional imageto obtain certain measurements.

In certain embodiments, the system can be configured to utilize one ormore two-dimensional x-ray and/or MRI image data. X-ray images can beroutine and widely available. In some embodiments, the system can beconfigured to provide one or more precise specifications and/ordimensions of one or more patient-specific screws based on analysis ofone or more two-dimensional x-ray images and/or MRI slices. In certainembodiments, the system can be configured to combine one or moremeasurements obtained from one or more two-dimensional x-ray imagesand/or MRI slices with one or more measurements and/or other featuresfrom the literature and/or other data to obtain increased accuracyand/or precision, for example in order to at least substantiallydecrease the total number of screws that may be required for aparticular surgery.

More specifically, in some embodiments, the system can be configured toutilize one or more features derived from spinal anatomical data,scientific/medical literature, surgeon preference or other input, and/orother data in order to obtain certain values and/or assumptions indetermining one or more parameters or variables for designingpatient-specific screws. Referring back to FIG. 12A, in someembodiments, the system can be configured to determine and/or obtain oneor more values and/or assumptions at block 1204 based at least in parton scientific or medical literature, spinal anatomical data, and/orsurgeon preference or input that can be stored in a database 1206. Theone or more values and/or assumptions can be specific for each vertebraof interest. For example, in certain embodiments, the system can beconfigured to obtain one or more values and/or assumptions regardingangulation of a screw in a sagittal plane in reference to an endplate ofthe vertebra in which the screw is inserted in (α), angle betweenvertebra axis and pedicle axis on a transverse plane (β) which can belevel-specific, ratio between screw length and screw insertion axislength (y/x), and/or ratio between vertebral body width (VBW) andminimum pedicle width (W) which can be level-specific.

FIG. 12B is a schematic diagram illustrating certain aspect(s) of anexample embodiment(s) of screw design, production, modification, and/orselection. In particular, as illustrated in schematic 1214 of FIG. 12B,in some embodiments, the system can be configured to assume and/orobtain angulation α of an implanted screw in reference to an endplate inwhich the screw is inserted. For example, in some embodiments, it can beassumed that an implanted screw will be parallel to an endplate based onscientific literature and/or spinal anatomical data. In other words,angulation α of a screw in a sagittal plane can be assumed to be equalor substantially equal to the considered vertebra (and upper vertebralendplate) angulation. As such, in certain embodiments, this angulation αcan be assumed to be zero, for example based on scientific literatureand/or spinal anatomical data. In some embodiments, the system can beconfigured to assign any other angle value to angulation α, for examplebased on surgeon habits and/or preferences and may be surgeon-specific.

Further, as illustrated in schematic 1216, in certain embodiments, thesystem can be configured to obtain and/or assume an angle β between thevertebra axis and the pedicle axis (in transverse plane) at each level,for example from scientific literature and/or spinal anatomical data.This angle β between the vertebra axis and the pedicle axis (intransverse plane) can be level-specific. In some embodiments, the systemcan be configured to assign any other angle value to angle β, forexample based on surgeon habits and/or preferences and may besurgeon-specific.

Similarly, as illustrated in schematic 1218, in some embodiments, thesystem can be configured to obtain and/or assume a ratio (y/x) betweenscrew length and screw insertion axis length (SIA), for example based onscientific literature and/or spinal anatomical data. For example, insome embodiments, the system can be configured to assume 70% penetrationof the vertebral body by a screw for optimal bone screw anchorage, asmay be suggested by scientific literature. In certain embodiments, SIAmay be surgeon-specific and/or may depend on a preference and/orsurgical goal of a particular patient. Surgeon preferences can also beconsidered for screw orientation in a transverse plane (in line withpedicle, convergent or even divergent). Based on a data-driven process,scientific literature, and/or surgeon preference, the system can beconfigured to assume SIA to be about 10%, about 20%, about 30%, about40%, about 50%, about 60%, about 70%, about 80%, about 90%, and/orwithin a range defined by two of the aforementioned values.

Further, as illustrated in schematic 1220, in certain embodiments, thesystem can be configured to obtain and/or assume a ratio betweenvertebral body width (VBW) and minimum pedicle width (W), for examplefrom scientific literature and/or spinal anatomical data. The ratiobetween VBW and W can be level-specific. As such, the system can beconfigured to obtain a ratio between VBW and W for each level vertebrain certain embodiments. In some embodiments, the system can beconfigured to assign any other angle value to VBW, W, and/or a ratiothereof, for example based on surgeon habits and/or preferences and maybe surgeon-specific.

Referring back to FIG. 12A, in some embodiments, the system can beconfigured to measure one or more parameters from the one or medicalimages at block 1208. In particular, the system can be configured tomeasure from the one or more medical images, one or more of a screwinsertion axis (SIA) projected length on the sagittal plane (SIAp)and/or vertebral body width (VBW) for each vertebra of interest. Inparticular, in certain embodiments, SIAp and/or VBW measurement(s) canbe obtained from a sagittal x-ray image and/or an MRI slice for eachvertebra of interest. In certain embodiments, the length of an endplatecan be measured from a sagittal x-ray image. In some embodiments, thelength from a pedicle entry to an anterior wall of the vertebra can beobtained. In certain embodiments, both endplate length and length from apedicle entry to an anterior wall of the vertebra can be measured foreach vertebra of interest.

In certain embodiments, based on such assumptions and data fromscientific literature and/or surgeon input and further based on SIApand/or VBW, the system can be configured to determine and/or estimatethe length and/or diameter of each patient-specific screw. Inparticular, in some embodiments, the system at block 1210 can beconfigured to determine the length(s) of each screw(s) for each level(s)of interest based in part on SIAp, VBW, angle between vertebrae axis andpedicle axis, and/or screw penetration ratio. In certain embodiments,the system at block 1212 can be configured to determine the diameter(s)of each screw(s) for each level(s) of interest based in part on SIAp,VBW, and/or ratio between VBW and a minimum pedicle width (W). In someembodiments, the VBW to W ratio can be assumed from the literature to beabout 10%, about 20%, about 30%, about 40%, about 50%, about 60%, about70%, about 80%, about 90%, and/or within a range defined by two of theaforementioned values.

In certain embodiments, the system can be configured to determine alength between a point of insertion of a screw on a vertebra and anopposing outer boundary of the vertebra or endplate thereof and use thesame or a percentage of such length as a not-to-exceed value for adesired length of a screw that is determined by the system for aparticular vertebra, for example as a system check after determining oneor more parameters for one or more patients-specific screws. Forexample, the system can be configured to ensure that, for a particularvertebra of interest, the length of a screw to be implanted in thatvertebra, as determined by the system, does not exceed about 100%, about95%, about 90%, about 85%, about 80%, about 75%, about 70%, about 65%,about 60%, and/or a percentage in between two of the aforementionedvalues of a determined length between a point of insertion of a screw onthat vertebra and an opposing outer boundary of the vertebra or endplatethereof. In some embodiments, the system can be configured to determinea minimum pedicle width (W) and utilize the same or a percentage thereofas a not-to-exceed value for a desired width of a screw that isdetermined by the system for a particular vertebra, for example as asystem check after determining one or more parameters for one or morepatients-specific screws. For example, the system can be configured toensure that, for a particular vertebra of interest, the width of a screwto be implanted in that vertebra, as determined by the system, does notexceed about 100%, about 95%, about 90%, about 85%, about 80%, about75%, about 70%, about 65%, about 60%, and/or a percentage in between twoof the aforementioned values of a determined minimum pedicle width (W).

As such, in some embodiments, the system can be configured to determinea desired diameter and/or length of a screw(s) to obtain sufficientanchorage for one or more vertebra of interest and/or every vertebra ora subset thereof for a specific patient and/or a specific vertebrathereof. Screw design, recommendation, and/or selection of certainscrews prior to surgery can be based on any one of the patient-specificfeatures and/or analysis discussed above. In certain embodiments, thesystem can be further configured to design, recommend, plan, and/orselect one or more patient-specific screws (and/or a material, type,length, and/or width thereof) based on a determine bone quality, bonedensity, age of patient, and/or gender of patient. In certainembodiments, the diameter of a patient-specific screw, as determined bythe system, can range from about 3.5 mm to about 10.5 mm. In someembodiments, such determined one or more screw parameters, such aslength, diameter, material, anchorage, or the like, can be used by thesystem to design, produce, and/or select, for example from apre-existing range or inventory, one or more patient-specific screws.

Intraoperative Tracking Sensors

Generally speaking, certain intraoperative imaging such as fluoroscopyand/or CT scans can be used for intraoperative assessment of spinalcurvatures and/or correction thereof. However, such processes generallyonly provide instantaneous vision/assessment of spinal curvatures. Assuch, it can be advantageous to allow live-tracking of spinalcurvatures/angulations to provide substantial assistance to the surgeon,thereby further allowing the surgeon to make further corrections to thespine as may be necessary under live control. At the same time, certainlive-tracking devices, such as those that may be based on optoelectronicpassive sensors, may disturb the surgeon's workflow as many additionalsteps may be required compared to usual surgery.

Accordingly, in some embodiments described herein, systems, device, andmethods are provided that allow for intraoperative monitoring. Inparticular, in certain embodiments, the system can be configured totrack a surgeon's performance in real-time, near real-time, and/or insubstantially real-time and further compare the same to the preoperativeplanning, while adding only a minor footprint on surgery workflow.

In some embodiments, the system can allow a surgeon to manipulate apatient's spine and follow one or more positions and/or one or moreorientations of one or more sensors that are attached to one or morevertebrae. One or more sensors attached to one or more vertebrae can beconfigured to provide tracking data relating to one or more positionsand/or orientations of the vertebra the sensor is attached to. As such,in certain embodiments, based on such tracking data and/or guidance dataderived therefrom, the surgeon can then manipulate the patient's spineuntil one or more sensor readings show that the positioning of the spineis optimal, desirable, and/or matches a predetermined plan.

In some embodiments, intraoperative imaging processes or techniques,such as fluoroscopy and/or CT scans can be used for intraoperativeimaging. For example, in certain embodiments, intraoperative fluoroscopycan be used to assess the position of screws regarding anatomystructures to provide intraoperative tracking. In certain embodiments,one or more sensors can be used in conjunction with one or more infraredcameras and/or electromagnetic detection. In some embodiments, theposition(s) and/or orientation(s) of the one or more sensors and/orbones can be identified by use of active sensors. In certainembodiments, one or more passive sensors can also be used.

In some embodiments, the system can be configured to identify theposition(s) and/or orientation(s) of one or more pedicle screws, and inturn one or more bones and/or vertebrae to which the one or more pediclescrews are attached thereto, by use of one or more active sensors. Assuch, in certain embodiments, the system is configured to utilize one ormore active sensors, without the need for any receivers to interpret theposition, orientation, and/or angulation of one or more sensors on acommon axes system. In other words, in some embodiment, the wholeintraoperative tracking system and/or device may include only one ormore sensors and one or more computer devices or systems treating thesignal of the one or more sensors and displaying one or moremeasurements obtained therefrom.

In some embodiments, a sensor, as the term is used herein, can comprisea power source, such as a battery, a wireless transmitter, and one ormore active and/or passive sensors for real-time tracking. In certainembodiments, the one or more sensors can comprise one or moreaccelerometers and/or one or more gyroscopes to provide one or moreinertial measurement units, such as in 6 degrees of freedom (DOF) and/or9 DOF. In some embodiments, the system can comprise one or more activesensors which are configured to be an inertial measurement unit in 6 DOFand/or 9 DOF. In some embodiments in which the system is configured toutilize one or more passive sensors, visual tracking can be utilized toprovide intraoperative tracking in real-time, near real-time, and/or insubstantially real-time. In other embodiments in which only activesensors are used, the system can be configured not to rely on visualtracking. Rather, the system can utilize wireless transmission of motiondata for intraoperative tracking in real-time, near real-time, and/or insubstantially real-time.

In certain embodiments, the system can be configured to determinerelative orientation and/or position of two or more sensors attached toa patient's spine to measure and/or calculate spinal curvature, forexample by interpreting independent sensor data. In particular, in someembodiments, the system can be configured to interpret independentsensor data obtained from two or more sensors, using the gravity forcevector as a common reference. In certain embodiments, two of the threeaxes of each central units can be assumed or considered to be on a planeparallel or substantially parallel with a determinate angle to thesagittal plane of the patient lying on the operating table. In otherwords, in certain embodiments, the position and/or orientation of two ormore sensors can be configured such that two of the three axes ofposition data to be collected by each sensor are on or assumed to be ona plane parallel or substantially parallel to the sagittal plane of apatient lying on the operating table. As such, the right positioning ofthe inertial unit can be mechanically obtained through a sensor/implantinterface in some embodiments.

In some embodiments, one or more sensors can be attached to everyvertebra, for example through one or more interfaces provided via one ormore implants/screws and/or directly to bone structures. In certainembodiments, one or more sensors can be attached to only a portion ofthe vertebrae. As such, in some embodiments, one or more sensors may beattached to only a subset of vertebra that can provide valuable positionand/or angular data of the spine. FIG. 13A is a schematic illustratingan example embodiment(s) of intraoperative tracking. As illustrated inFIG. 13A, in some embodiments, one or more sensors 1302 may be attachedonly to certain vertebrae, for example to which a spinal road 1304 isimplanted. For example, in certain embodiments, one or more sensors maybe attached to S1, L1 and T4 vertebrae to assess L1-S1 lordosis and/orT4-T12 kyphosis.

In some embodiments in which one or more sensors are directly linkedand/or attached to one or more screws, the system can be configured toassume that angulation of a screw in a sagittal plane is substantiallyequal to the vertebra (or superior endplate) angulation. Optionally, incertain embodiments, intraoperative fluoroscopic images can be used toassess the position of screws regarding anatomic structures, such asvertebral endplates, in the sagittal plane, as well as other planes insome embodiments.

FIGS. 13B-13G illustrate example embodiments of screws and/or sensorsthat can be used for intraoperative tracking. In some embodiments, oneor more screws and/or other implants can be mono-axial and/orpoly-axial. FIGS. 13B-13E illustrate example embodiments of mono-axialscrews, while FIGS. 13F-G illustrate example embodiments of poly-axialscrews.

In certain embodiments where one or more mono-axial screws are used, thesystem can be configured to follow the position and/or angle of everyimplanted screw, thereby following the position of a vertebrae based onthe screw position. A mono-axial screw may comprise only one sensor1302, based on the assumption that every movement of the screw is due torigid movement of the vertebrae. In certain embodiments, a mono-axialscrew may comprise one or more sensors 1302.

In some embodiments, a poly-axial screw can comprise one or more sensorsand/or two or more sensors 1302, for example to be able to determine ifa particular motion or movement is due to rigid movement of thevertebrae itself or at least partially or wholly because of motionbetween the different portions of the screws, such as in and outside thevertebra, or non-rigid movement. In some embodiments, the system can beconfigured to determine that a particular movement is rigid movement ifthere is correlation between the two or more sensor readings.

In some embodiments, a top portion of a screw and/or other implant cancomprise one or more active and/or passive sensors. In certainembodiments, the top portion of a screw and/or other implant can alsocomprise a power source, such as a battery, and/or wireless transmitter,as well as one or active and/or passive sensors. In some embodiments,the top portion can be broken off later during surgery and notimplanted. The sensor 1302, or at least one or more portions thereof,can then be reused, thrown away, and/or repurposed for future use. Forexample, in the illustrated example embodiments in FIGS. 13B-13E, thesystem can be configured to utilize one or more monoaxial screws,polyaxial screws, and/or other implants, each comprising an attachingportion 1306 and a top portion 1308. The top portion 1308 can comprise asingle sensor 1302 for certain screws. Similarly, in the illustratedexample embodiments in FIGS. 13F-G, the system can be configured toutilize one or more poly-axial screws and/or other implants, eachcomprising an attaching portion 1306 and a top portion 1308. Asillustrated, in certain embodiments, the top portion 1308 can comprisetwo or more sensors 1302 each for certain screws.

In some embodiments, an intraoperative tracking system or device canrequire at least two or more screws to be attached to the vertebrae,wherein each of the two or more screws comprises at least one sensor. Incertain embodiments, an intraoperative tracking system or device canrequire at least one, two, three, four, five, six, seven, eight, nine,and/or ten screws comprising one or more sensors to be attached to thevertebrae. In some embodiments, an intraoperative tracking system ordevice can require a certain range of numbers of screws comprising atleast one sensor, wherein the range is defined by two of theaforementioned values.

In certain embodiments, once one, two, three, four, and/or more screwscomprising at least one sensor are attached to the vertebrae, the systemcan be configured to obtain one or more sensor readings of the currentposition(s), orientation(s), and/or angle(s) of one or more screws andthus vertebrae. Based on the reading(s) from the one or more sensorsand/or guidance generated therefrom, a surgeon can further manipulatethe patient's spine as desired. For example, in some embodiments, theintraoperative tracking system and/or device can be configured tocontinuously and/or periodically provide updated tracking data and/oranalysis therefrom, such that the surgeon can manipulate the patient'sspine until one or more sensor readings show that one or morepositioning and/or orientation of the spine are optimal and/or matchesor substantially matches a pre-determined plan.

In some embodiments, the system can also be configured to provide tips,guidance, and/or suggestions to the surgeon to manipulate the spine in acertain manner and/or direction, for example to reach and/or moreclosely follow the predetermined plan. In some embodiments, a surgeoncan implant the spinal rod through the one, two, three, four, and/ormore screws once an optimal or desired configuration of the spine isobtained. In certain embodiments, after rod implantation, the topportion of screw that comprises the one or more sensors can be brokenoff.

In certain embodiments, the one or more sensors are not provided as partof screws; rather one or more surgical tools, which can eventually beused to attach screws to the vertebrae, can comprise one or moresensors. For example, a screwdriver, nut driver, or other specific orusual surgical tool configured to attach a pedicle screw, anchorage,and/or other implant can comprise one or more active and/or passivesensors for intraoperative tracking purposes. In some embodiments, anintraoperative tracking system can require at least one, two, three,four, five, six, seven, eight, nine, and/or ten surgical tools tocomprise one or more sensors. In certain embodiments, an intraoperativetracking system or device can require a certain range of numbers oftools to comprise at least one sensor, wherein the range is defined bytwo of the aforementioned values.

In some embodiments, a surgical tool comprising one or more sensors forintraoperative tracking purposes can comprise a button or othersignaling mechanism that measures and/or stores the current positionand/or orientation data of the surgical tool, for example in 6 DOFand/or 9 DOF. As such, in certain embodiments, once a screw, anchorage,or other implant is put in place, such as attached to a vertebra, usingsuch surgical tool, the surgeon or other medical personnel can activatethe sensor in the tool, thereby detecting and/or providing orientationand/or position data at that time. As such, in some embodiments, theintraoperative tracking system can be configured to provide data frozenin time rather than providing real-time tracking data.

FIGS. 14A-14E illustrate example embodiments of tools and/or sensorsthat can be used for intraoperative tracking. As illustrated in FIG.14A, in some embodiments, a screwdriver 1400 can comprise one or moresensors 1302 and/or other electronic components for intraoperativetracking. In certain embodiments, as illustrated in the exampleembodiment of FIG. 14A, the one or more sensors 1302 and/or otherelectronic components can be located in a shaft 1402 of a screwdriver.In some embodiments, as illustrated in the example embodiment of FIG.14B, the one or more sensors 1302 and/or other electronic components canbe located in a handle 1404 of a screwdriver. Further, as illustrated inthe example embodiment of FIG. 14C, a nut driver 1406 can comprise oneor more sensor 1302, for example in the shaft 1408 and/or in the handle.

In some embodiments, as illustrated in the example embodiments of FIGS.14D and 14E, an additional housing structure 1410 configured to becoupled to a screw 1306, nut, anchorage, and/or other implant cancomprise one or more active and/or passive sensors 1302. In certainembodiments, the additional housing structure 1410 can also comprise apower source, such as a battery, and/or wireless transmitter, as well asone or active and/or passive sensors 1302. In some embodiments, theadditional housing structure 1410 can be configured to be used and/orcoupled to a removable handle 1412. The additional housing structure1410 can be attached to a screw 1306 and/or other pedicle anchorage orimplant. As the spine is adjusted during surgery, the one or moresensors 1302 can provide data relating to the orientation and/orposition of the screw 1306 as the additional housing structure 1410 isstill attached to the screw 1306. After the spine is adjusted to anacceptable level, a spinal rod 1304 can be inserted, and the screw 1306and/or other pedicle anchorage or implant can be affixed to thevertebrae using the removable handle 1412, after which, the additionalhousing structure 1410 can then be removed. In some embodiments, theadditional housing structure 1410 can provide additional space or volumefor the one or more sensors, power source, and/or wireless transmitterto be placed.

Predictive Modeling

In some embodiments, the system is configured to generate one or morepredictive models or algorithms for surgical operations. In certainembodiments, the one or more predictive models and/or algorithms areconfigured to predict one or more surgical parameters and/or variablesthat may result from a surgical procedure, for example of the spine. Insome embodiments, the predictive models and/or algorithms are configuredto generate a surgical plan for achieving desired surgical outcomes. Forexample, the systems disclosed herein can be configured to accesspreoperative patient input data and generate a surgical plan forimplanting a spinal rod into the patient where the generated surgicalplan that is personalized for the patient is configured to generate anoptimal post-surgical spine curvature for the particular patient.

When a patient undergoes surgery by a doctor, the surgical outcomes canbe generally determined based on the surgeon's estimations and priorsurgical experience. For example, when a spinal rod is implanted into apatient, the surgeon can analyze the patient's body and othercharacteristics. Based on these observations, the surgeon can provide ageneral estimate and/or select certain surgical parameters that thesurgeon believes will result in a better spinal curvature for thepatient post-surgery. However, in reality, the surgeon's estimations andselected surgical parameters may not result in the most desired oroptimal surgical outcomes.

For example, when performing a spinal surgery for improving a patient'sspinal curvature, the doctor can select a curvature for the spinal rodto be implanted into a patient. The rod curvature selection can bedetermined and/or estimated by the surgeon based on the doctor'sobservations of the patient, and such determinations and estimations mayresult in the patient having a spine curvature that is less than optimalafter the surgery. Accordingly, it can be beneficial for a surgeonand/or a patient to have a system that could predict surgical parameterspost-surgery based on pre-operative patient characteristics. Forexample, it would be helpful to determine, before performing spinalsurgery, one or more optimal surgical parameters that should be utilizedin a surgical plan in order to achieve the optimal spinal curvaturepost-surgery for a particular patient with certain characteristics.Certain systems, methods, and devices disclosed herein are configured toaddress the foregoing issues.

In particular, in some embodiments, the system can be configured toaccess pre-operative patient characteristics and input one or more suchvariables into a predictive algorithm. In certain embodiments, thesystem can be configured to utilize the predictive algorithm to generateone or more surgical plans having one or more specific surgicalparameters that are predicted to generate an optimal post-surgicaloutcome for the patient. For example, the system can be configured toreceive one or more patient characteristics, such as preoperative spinalcurvatures and angles, patient age, genetic mapping or geneticconditions, and/or other variables. In particular, the existence ofcertain genes may have a correlation with a particular condition, suchas scoliosis, and/or surgical outcome. The system can be configured toutilize such patient characteristics and/or variables for inputting intoa predictive algorithm. The system can be configured to output based onthe predictive algorithm specific surgical parameters, such as theoptimal spinal rod curvature and/or instrumentation positions and/orother variables for achieving the optimal spinal curvature post-surgeryfor the patient.

In some embodiments, the system is configured to utilize the one or morepredictive algorithms to generate a predictive post-surgical outcome.For example, the system can be configured to access one or more patientcharacteristics as well as surgical parameters that a surgeon intends touse in a surgical plan. In some embodiments, the system can beconfigured to utilize the predictive algorithm to determine thepost-surgical outcome that will result from the surgical parametersassociated with the surgical plan. For example, the system can beconfigured to access patient characteristics, such as preoperativespinal curvature and/or angles, patient age, genetic conditions, and/orany other variable. The system can also be configured to access thecurvature of the spinal rod that the surgeon intends to implant into thepatient. In some embodiments, the system can be configured to generate apredictive post-surgical spinal curvature for the patient based on theinputted of variables, in this example, the patient characteristics andthe curvature of the spine rod to be implanted into the patient.

As one of ordinary skill will appreciate, the systems disclosed hereincan be applied to a myriad of surgical procedures and is not intended tobe limited to spinal surgeries. For example, the systems disclosedherein can be applied to any kind of surgery, including but not limitedorthopedic surgeries, such as, neck, head, hand, foot, leg, and armsurgeries.

In some embodiments, the system is configured to generate a predictivemodel for predicting post-surgical parameters. In some embodiments, thesystem is configured to generate the predictive model by selecting adataset comprising preoperative and/or postoperative data for one ormore patients. As a non-limiting example, in some embodiments, thesystem is configured to identify all cases with PJK (proximal junctionalkyphosis) and remove such cases from the dataset. In some embodiments,the system is configured to remove all pediatric cases from the dataset.In some embodiments, removal of the pediatric cases can be based onprior knowledge of the cases in the dataset.

In some embodiments, the system is configured to split data based oninstrumented levels into different groups. For example, the system canbe configured to split the dataset into a first group wherein there isinstrumentation at L1-L5 and at S1-Iliac, and into a second groupwherein there is instrumentation at T10-T12 and at S1-Iliac. For eachgroup, the system, in some embodiments, is configured to split data intoa training set and a testing set (for example, ˜75% of the data for thetraining set and ˜25% of the data for the testing set).

In some embodiments, the system is configured to select one or moreinput parameters, for example, age, PI pre-op value, PT pre-op value, LLpre-op value, TK pre-op value, SVA pre-op value, lower instrumentedlevel, upper instrumented level, LL post-op target value, surgeon,weight, shape of the preoperative spline, preoperative x-ray, or thelike. In some embodiments, the system is configured to standardize therange of input parameters and/or utilize a scaling methodology.

In some embodiments, the system is configured to standardize the databased on the training set. In some embodiments, the system is configuredto select a first model type from a plurality of model types, such asfor example linear models, neural networks, deep learning, SVR, or thelike. In some embodiments, the system is configured to select the bestmodel using cross validation. In some embodiments, the system isconfigured to perform cross validation by splitting the data set into anew training set and a new testing set. In some embodiments, the systemis configured to train the model with the new training set and evaluatethe results with the new testing set.

In some embodiments, the system is configured to repeat the trainingprocess until each data has been once and only once in a testing set. Insome embodiments, the system is configured to train the model selectedwith the training set. In some embodiments, the system is configured toutilize a linear model named least-angle regression (LARS) withregularization and variable selection algorithm least absolute shrinkageand selection operator (LASSO). In some embodiments, the system isconfigured to test the trained model with the testing set to determinewhether the trained model satisfies an accuracy threshold level. In someembodiments, the system is configured to utilize the trained model tocompare with a proposed surgical plan to determine whether the surgicalplan is optimal for the patient and/or will produce optimalpost-operative surgical results for the patient having certain patientcharacteristics.

FIG. 15 is a flowchart illustrating an example embodiment(s) ofpredictive modeling. In the illustrated example embodiment, the systemcan be configured to access and/or retrieve one or more preoperative,intraoperative, and/or postoperative data sets at block 1502. The one ormore datasets can be accessed and/or retrieved from one or moredatabases, such as the plan database 216 and/or operation database 218among others.

In certain embodiments, the system can be configured to determinewhether the retrieved or accessed dataset comprises postoperative dataat block 1504. If a dataset comprises postoperative data, the system canbe configured to identify one or more variables of interest, such asthose described herein, from the postoperative data and/or relatedpreoperative and/or intraoperative datasets at block 1506. Based in parton the identified one or more variables, the system can be configured totrain a predictive modeling algorithm at block a 1508 according to oneor more processes or techniques described herein. This training processand/or technique and/or portion thereof can be repeated as necessary.For example, in certain embodiments, the system can be configured torepeat the training algorithm and/or a portion thereof as additionaldata becomes available, such as data from an additional patient and/oradditional postoperative data from a known patient or the like.

In some embodiments, if the retrieved or accessed dataset is for a newcase, and as such does not comprise postoperative data the system can beconfigured to apply one or more predictive modeling algorithms to suchinput preoperative data. In particular, in certain embodiments, thesystem can be configured to identify one or more variables from theinput preoperative data and/or compare the same with one or more otherdatasets at block 1510. In some embodiments, based on the comparisonand/or other data analysis, the system can apply one or more predictivemodeling algorithms to the input preoperative data. Subsequently, insome embodiments, the system can be configured to generate one or morepredicted surgical outcomes and/or plan and/or one or more variablesthereof based on the predictive model at block 1512. In certainembodiments, based at least in part on the resulting surgical planand/or one or more variables thereof, the system can be configured toproduce, modify, select, and/or provide guidance for selection of one ormore spinal implants at block 1514, such as spinal rods, cages, and/orscrews.

Other Embodiments for Predictive Modeling

In some embodiments, the system is configured to perform acomputer-implemented method that is configured to generate a predictivemodel for determining post-operative parameters, such as thoracickyphosis or pelvic tilt, wherein the computer-implemented methodcomprises accessing a dataset from an electronic database, the datasetcomprising data about the patient (for example, an X-ray images orclinical information) and the surgery strategy (for example, upperinstrumented vertebra, lower instrumented vertebra, or the like). Insome embodiments, the computer-implemented method is configured todefine in the dataset which parameters should be inputs of the model andwhich parameters should be outputs of the model. For example, outputs ofthe model can be the parameters that the system is configured to bepredicted.

In some embodiments, the system is configured to optionally divide thedataset into a plurality of categories based on the spinal surgerydomain knowledge, for example, the dataset can be configured to separateadult cases and pediatric cases. In some embodiments, the system can beconfigured to generate a predictive model for each category. In someembodiments, the system is configured to separate the data into a firstsubcategory and a second subcategory, wherein the first subcategory isused for training and the second subcategory is for testing thepredictive model. In some embodiments, the system is configured tostandardize the data using the first category.

In some embodiments, the system is configured to select a modelalgorithm, for example, neural network, support vector regression,linear models, or the like. In some embodiments, the system isconfigured to select the model based on using a cross validationstrategy. In some embodiments, the system is configured to input one ormore input values into the model based on the first subcategory to trainthe statistical models based on the output values of the firstsubcategory. In some embodiments, the system is configured to input oneor more input data values in the generated trained model and compare theoutputs generated by the model with the output values of the firstsubcategory. In certain embodiments, based on the foregoing comparison,a model is generated and the performance of the model is known. In someembodiments, the system is configured to store the first trainedstatistical model in a data repository. In some embodiments, the systemcomprises a computer processor and electronic memory. In certainembodiments, one or more of the above-identified processes or techniquesare repeated for each of the categories defined by when dividing thedataset based on a spinal surgery domain knowledge block as describedabove.

In some embodiments, the system is configured to perform acomputer-implemented method for generating a predictive model forestimating post-operative parameters, wherein the computer-implementedmethod comprises accessing a dataset from an electronic database, thedataset comprising data collected from one or more patients and spinalsurgical strategy employed for the one or more patients. In someembodiments, the system is configured to divide the dataset into one ormore categories based on spinal surgery domain knowledge. In someembodiments, the system is configured to for each category, separate thedata into a first subcategory and a second subcategory, wherein thefirst subcategory is used for training and the second subcategory is fortesting the predictive model.

In some embodiments, the system is configured to standardize the data inthe first subcategory. In some embodiments, the system is configured toselect a model algorithm to the data in the first subcategory. In someembodiments, the system is configured to input a first set of inputvalues from the first subcategory into the model algorithm to train thepredictive model based on a first set of output values from the firstsubcategory. In some embodiments, the system is configured to input asecond set of input values from the second subcategory into the trainedpredictive model and comparing results generated by the trainedpredictive model with a second set of output values from the secondsubcategory. In some embodiments, the system is configured to store in adata repository the trained predictive model for implementation orfuture use. In some embodiments, the post-operative parameters compriseone or more of thoracic kyphosis or pelvic tilt. In some embodiments,the system comprises a computer processor and electronic memory.

In some embodiments, the data collected from one or more patientscomprise one or more of an x-ray or clinical information. In someembodiments, the surgical strategy employed for the one or more patientscomprises data relating to one or more of upper instrumented vertebra orlower instrumented vertebra. In some embodiments, the spinal surgerydomain knowledge comprises one or more of adult cases or pediatriccases. In some embodiments, the model algorithm comprises one or more ofa neural network, support vector regression, or linear model or thelike. In some embodiments, the model algorithm is selected using across-validation strategy.

In some embodiments, the system is configured to perform acomputer-implemented method for generating a predictive model forestimating post-operative thoracic kyphosis and pelvic tilt parameters,wherein the computer-implemented method comprises accessing a datasetfrom an electronic database, the dataset comprising data from spinalsurgeries, wherein the spinal surgeries involve at least an upperinstrumented vertebra and a lower instrumented vertebra. In someembodiments, the system is configured to analyze the dataset to dividethe dataset into a plurality of categories, the plurality of categoriescomprising a first category comprising data from surgeries, wherein theupper instrumented vertebra is positioned between L1 and L5 vertebraeand the lower instrumented vertebra is positioned between S1 and iliac.

In some embodiments, the system is configured to select the firstcategory, and access the data from the surgeries, the data comprisingone or more of patient ages, pelvic incidence pre-operative values,pelvic tilt pre-operative values, lumbar lordosis pre-operative values,thoracic kyphosis pre-operative values, sagittal vertical axispre-operative values, lower instrumented vertebra values, upperinstrumented vertebra values, or lumbar lordosis post-operative targetvalues for each of the surgeries in the first category. In someembodiments, the system is configured to standardize the data in thefirst category.

In some embodiments, the system is configured to separate the data intoa first subcategory and a second subcategory, wherein the firstsubcategory is used for training and the second subcategory is fortesting the predictive model for determining the post-operative thoracickyphosis and pelvic tilt parameters. In some embodiments, the system isconfigured to input pre-operative data values in the first subcategoryinto a plurality of statistical models to train the statistical modelsbased on the post-operative data values. In some embodiments, the systemis configured to input pre-operative data values in the secondsubcategory into the plurality of trained statistical models andcomparing output values from the plurality of trained statistical modelswith post-operative data values in the second subcategory.

In some embodiments, the system is configured to select a first trainedstatistical model from the plurality of trained statistical models,wherein the first trained statistical model generated an output valuesnearest to the post-operative data values based on the comparing. Insome embodiments, the system is configured to store in electronic memorythe first trained statistical model. In some embodiments, the systemcomprises a computer processor and electronic memory.

In some embodiments, the system is configured to perform acomputer-implemented method for generating a surgical plan based on apredictive model for estimating post-operative parameters, thecomputer-implemented method comprising accessing one or more medicalimages of a portion of a spine of a patient. In some embodiments, thesystem is further configured to analyze the one or more medical imagesto determine one or more pre-operative variables relating to the spineof the patient, wherein the one or more pre-operative variables compriseat least one of UIL, LIL, age of the patient, pelvic incidencepre-operative values, pelvic tilt pre-operative values, lumbar lordosispre-operative values, thoracic kyphosis pre-operative values, orsagittal vertical axis pre-operative values. In some embodiments, thesystem is configured to generate a prediction of one or morepost-operative variables based at least in part on applying a predictivemodel, wherein the predictive model is generated by one or more of thefollowing processes.

In some embodiments, the predictive model is configured to access adataset from an electronic database, the dataset comprising datacollected from one or more previous patients and spinal surgicalstrategy employed for the one or more previous patients. In someembodiments, the predictive model is configured to divide the datasetinto one or more categories based on spinal surgery domain knowledge. Insome embodiments, the predictive model is configured to standardize thedata in the first subcategory.

In some embodiments, the predictive model is configured to select amodel algorithm to the data in the first subcategory. In someembodiments, the predictive model is configured to input a first set ofinput values from the first subcategory into the model algorithm totrain the predictive model based on a first set of output values fromthe first subcategory. In some embodiments, the predictive model isconfigured to input a second set of input values from the secondsubcategory into the trained predictive model and comparing resultsgenerated by the trained predictive model with a second set of outputvalues from the second subcategory.

In some embodiments, the post-operative parameters of the predictivemodel comprise one or more of thoracic kyphosis or pelvic tilt. In someembodiments, the system is configured to generate a surgical plan basedat least in part on the predicted one or more post-operative variablesgenerated by the predictive model. In some embodiments, the surgicalplan comprises at least one of a number of cages for implantation,location of implantation of cages, length of a spinal rod forimplantation, or curvature of the spinal rod. In some embodiments, thesystem comprises a computer processor and electronic memory.

In some embodiments, the system is configured to perform acomputer-implemented method for generating a surgical plan based on apredictive model for estimating post-operative thoracic kyphosis andpelvic tilt parameters, the computer-implemented method comprisingaccessing one or more medical images of a portion of a spine of apatient. In some embodiments, the system is further configured toanalyze the one or more medical images to determine one or morepre-operative variables relating to the spine of the patient, whereinthe one or more pre-operative variables comprise at least one of UIL,LIL, age of the patient, pelvic incidence pre-operative values, pelvictilt pre-operative values, lumbar lordosis pre-operative values,thoracic kyphosis pre-operative values, or sagittal vertical axispre-operative values. In some embodiments, the system is configured togenerate a prediction of one or more post-operative variables based atleast in part on applying a predictive model, wherein the predictivemodel is generated by one or more of the following processes.

In some embodiments, the predictive model is configured to access adataset from an electronic database, the dataset comprising data fromspinal surgeries, wherein the spinal surgeries involve at least an upperinstrumented vertebra and a lower instrumented vertebra. In someembodiments, the predictive model is configured to analyze the datasetto divide the dataset into a plurality of categories, the plurality ofcategories comprising a first category comprising data from surgeries,wherein the upper instrumented vertebra is positioned between L1 and L5vertebrae and the lower instrumented vertebra is positioned between S1and iliac.

In some embodiments, the predictive model is configured to select thefirst category, and access the data from the surgeries, the datacomprising one or more of patient ages, pelvic incidence pre-operativevalues, pelvic tilt pre-operative values, lumbar lordosis pre-operativevalues, thoracic kyphosis pre-operative values, sagittal vertical axispre-operative values, lower instrumented vertebra values, upperinstrumented vertebra values, or lumbar lordosis post-operative targetvalues for each of the surgeries in the first category. In someembodiments, the predictive model is configured to standardize the datain the first category.

In some embodiments, the predictive model is configured to separate thedata into a first subcategory and a second subcategory, wherein thefirst subcategory is used for training and the second subcategory is fortesting the predictive model for determining the post-operative thoracickyphosis and pelvic tilt parameters. In some embodiments, the predictivemodel is configured to input pre-operative data values in the firstsubcategory into a plurality of statistical models to train thestatistical models based on the post-operative data values. In someembodiments, the predictive model is configured to input pre-operativedata values in the second subcategory into the plurality of trainedstatistical models and comparing output values from the plurality oftrained statistical models with post-operative data values in the secondsubcategory.

In some embodiments, the predictive model is configured to select afirst trained statistical model from the plurality of trainedstatistical models, wherein the first trained statistical modelgenerated an output values nearest to the post-operative data valuesbased on the comparing. In some embodiments, the predicted one or morepost-operative variables comprises at least one of lumbar lordosispost-operative target values, thoracic kyphosis post-operative values,or sagittal vertical axis post-operative values. In some embodiments,the system is configured to generate a surgical plan based at least inpart on the predicted one or more post-operative variables. In someembodiments, the surgical plan comprises at least one of a number ofcages for implantation, location of implantation of cages, length of aspinal rod for implantation, or curvature of the spinal rod. In someembodiments, the system comprises a computer processor and electronicmemory.

Data Elements/Parameters for Predictive Modeling

In some embodiments, in order to perform one or more processes ortechniques relating to predictive modeling, the system can be configuredto receive, access, and/or obtain one or more of the following dataelements or parameters that can be collected from one or more patients.

In particular, in certain embodiments, the system can be configured toreceive, access, and/or obtain one or more demographic characteristics,such as for example, age at surgery, gender, height, weight, activitylevel, date of narcotics, disability, education, home care requirements,insurance coverage, job, race, date of return to work/school/sport,socioeconomic status, and/or the like.

In some embodiments, the system can be configured to receive, access,and/or obtain one or more patient-reported outcomes, such as forexample, Oswestry Disability Index (ODI), Neck Disability Index (NDI),Scoliosis Research Society (SRS-22), Nurick, and/or the like.

In certain embodiments, the system can be configured to receive, access,and/or obtain one or more radiographic parameters, such as for example,preoperative and/or postoperative data such as T4-T12 TK (=ThoracicKyphosis), L1-S1 LL (=Lumbar Lordosis), Lateral C7 to Sacrum SVA(=Sagital Vertical Axis), PT (=Pelvic Tilts). PI (Pelvic Incidence),L1-S1TK (=Thoracic Kyphosis), and/or the like.

In some embodiments, the system can be configured to receive, access,and/or obtain one or more other radiographic parameters as well, such asApical Translation ThL/Lumbar Curve-CSVL, C2T1 Pelvic Angle(=CTPA,^(∘)), C2C7 SVA (mm) (=Sagital Vertical Axis), Cervical Lordosis,Lenke Classification, Proximal Jonctionnal Kelphosis (PJK), Rod Tracing,SS, T1 Slope (T1S,^(∘)) T1 Tilt Angle and Direction, T10-L2, T12-S1Lombar Lordodis, T2-T12, T2-T5, T5-T12 Thoracic Kyphosis, Th Apex, ThBend, Th Curve, Th Curve Levels, (Th/L Lumbar Apex, Th/L Lumbar Curve,Th/L Lumbar Curve Direction of curve. Th/L Lumbar Curve Levels), T1Pelvic Angle (TPA), Anatomical Kyphosis, Anatomical Lordosis, CobbAngles, Coordinates of all vertebra corners in the saggital and coronalplanes and the femoral heads, Pre-op or post-operative datas like ApicalTranslation Th Curve-C7 Plumb, Apical Translation Th Curve—CSVL,Computerized tomography Performed, Disc Angulation Below EspaceInstrumental Vertebral (EIV), EIV Angulation, EIV Translation, CoronalC7 to CSVL, T1S-CL(^(∘)), TH CUrve—Direction od Curve, Tri-RadiateCartilage, Upper Th Bend, Upper Th Curve, Upper Th Curve—Direction ofCurve, Upper Th Curve—Levels., External Auditory Meadus, PelvicObliquity, Pelvic Version, Acetabular Index, and/or the like.

In some embodiments, the systems disclosed herein can be configured togenerate spinal surgical strategies comprising one or more surgical dataparameters, such as Instrumentation Material. Instrumentation Size,Instrumentation Type, Lowermost Instrumented, MIS (=Minimal InvasiveSurgery), Number of Levels, Osteotomies Performed, Rod Bending Degreesand/or Angles, Rod Cutting Parameters, Uppermost InstrumentedParameters, Upper Instrumented Vertebrae (UIV), Lower InstrumentedVertebrae (LIV), Surgeon, surgical techniques (in some embodiments, usemachine learning algorithms to analyze surgeon's surgical techniques tobe able to simulate the surgery and the rod that will match surgeon'sexpectations), radiography as an image, scanner, MRI (image or set ofimages), and/or the like.

In an example embodiment, a first set of input values for preoperativeand/or postoperative data can include the following: T4-T12 TK(=Thoracic Kyphosis), L1-S1 LL(=Lumbar Lordodis), Lateral C7 to Sacrum(SVA) (=Sagittal Vertical Axis), Lowermost Instrumented Vertebrae (LIV),Uppermost Instrumented Vertebrae (UIV), Pelvic Tilt, Age at the time ofsurgery, and Pelvic Incidence (PI).

In an example embodiment, a first set of output values for preoperativeand/or postoperative data can include the following: T4-T12 TK(=Thoracic Kyphosis), L1-S1 LL (=Lumbar Lordosis), and Pelvic Tilt.

System

FIG. 16 is a schematic diagram illustrating an embodiment of a systemfor developing patient-specific spinal treatments, operations, andprocedures. In some embodiments, a main server system 1602 may becomprised of an image analysis module 1604, a case simulation module1606, an intra-operative tracking module 1608, a data utilization module1610, a predictive modeling module 1628, a plan database 1612, anoperation database 1614, a surgeon database 1616, and/or a literaturedatabase 1618. The main server system can be connected to a network1620. The network can be configured to connect the main server to one ormore implant production facility systems 1626, one or more medicalfacility client systems 1622, and/or one or more user access pointsystems 1624.

The image analysis module 1604 may function by providing image analysisand/or related functions as described herein. The case simulation module1606 may function by performing surgical planning, case simulation,and/or related functions as described herein. The intra-operativetracking module 1608 may function by performing intra-operative trackingand/or related functions as described herein. The data utilizationmodule 1610 may function by retrieving and/or storing data from and toone or more databases and/or related functions as described herein. Thepredictive modeling module 1628 may function by performing one or morepredictive modeling processes as described herein.

The plan database 1612 may provide a collection of all plans that havebeen generated by the system and/or related data. The operation database1614 may provide a collection of all surgical operations that have beenperformed utilizing the system and/or related data. The surgeon database1616 may provide a collection of all surgeons who have utilized thesystem and/or related data, such as surgeon preferences, skill levels,or the like. The literature database 1618 may provide a collection ofscientific literature related to spinal surgery.

Computer System

In some embodiments, the systems, processes, and methods describedherein are implemented using a computing system, such as the oneillustrated in FIG. 17. The example computer system 1702 is incommunication with one or more computing systems 1720 and/or one or moredata sources 1722 via one or more networks 1718. While FIG. 17illustrates an embodiment of a computing system 1702, it is recognizedthat the functionality provided for in the components and modules ofcomputer system 1702 may be combined into fewer components and modules,or further separated into additional components and modules.

The computer system 1702 can comprise a patient-specific spinaltreatment, operations, and procedures module 1714 that carries out thefunctions, methods, acts, and/or processes described herein. Thepatient-specific spinal treatment, operations, and procedures module1714 is executed on the computer system 1702 by a central processingunit 1706 discussed further below.

In general the word “module,” as used herein, refers to logic embodiedin hardware or firmware or to a collection of software instructions,having entry and exit points. Modules are written in a program language,such as JAVA, C or C++, PYPHON or the like. Software modules may becompiled or linked into an executable program, installed in a dynamiclink library, or may be written in an interpreted language such asBASIC, PERL, LUA, or Python. Software modules may be called from othermodules or from themselves, and/or may be invoked in response todetected events or interruptions. Modules implemented in hardwareinclude connected logic units such as gates and flip-flops, and/or mayinclude programmable units, such as programmable gate arrays orprocessors.

Generally, the modules described herein refer to logical modules thatmay be combined with other modules or divided into sub-modules despitetheir physical organization or storage. The modules are executed by oneor more computing systems, and may be stored on or within any suitablecomputer readable medium, or implemented in-whole or in-part withinspecial designed hardware or firmware. Not all calculations, analysis,and/or optimization require the use of computer systems, though any ofthe above-described methods, calculations, processes, or analyses may befacilitated through the use of computers. Further, in some embodiments,process blocks described herein may be altered, rearranged, combined,and/or omitted.

The computer system 1702 includes one or more processing units (CPU)1706, which may comprise a microprocessor. The computer system 1702further includes a physical memory 1710, such as random access memory(RAM) for temporary storage of information, a read only memory (ROM) forpermanent storage of information, and a mass storage device 1704, suchas a backing store, hard drive, rotating magnetic disks, solid statedisks (SSD), flash memory, phase-change memory (PCM), 3D XPoint memory,diskette, or optical media storage device. Alternatively, the massstorage device may be implemented in an array of servers. Typically, thecomponents of the computer system 1702 are connected to the computerusing a standards based bus system. The bus system can be implementedusing various protocols, such as Peripheral Component Interconnect(PCI), Micro Channel, SCSI, Industrial Standard Architecture (ISA) andExtended ISA (EISA) architectures.

The computer system 1702 includes one or more input/output (I/O) devicesand interfaces 1712, such as a keyboard, mouse, touch pad, and printer.The I/O devices and interfaces 1712 can include one or more displaydevices, such as a monitor, that allows the visual presentation of datato a user. More particularly, a display device provides for thepresentation of GUIs as application software data, and multi-mediapresentations, for example. The I/O devices and interfaces 1712 can alsoprovide a communications interface to various external devices. Thecomputer system 1702 may comprise one or more multi-media devices 1708,such as speakers, video cards, graphics accelerators, and microphones,for example.

The computer system 1702 may run on a variety of computing devices, suchas a server, a Windows server, a Structure Query Language server, a UnixServer, a personal computer, a laptop computer, and so forth. In otherembodiments, the computer system 1702 may run on a cluster computersystem, a mainframe computer system and/or other computing systemsuitable for controlling and/or communicating with large databases,performing high volume transaction processing, and generating reportsfrom large databases. The computing system 1702 is generally controlledand coordinated by an operating system software, such as z/OS, Windows,Linux, UNIX, BSD, SunOS, Solaris, MacOS, or other compatible operatingsystems, including proprietary operating systems. Operating systemscontrol and schedule computer processes for execution, perform memorymanagement, provide file system, networking, and I/O services, andprovide a user interface, such as a graphical user interface (GUI),among other things.

The computer system 1702 illustrated in FIG. 17 is coupled to a network1718, such as a LAN, WAN, or the Internet via a communication link 1716(wired, wireless, or a combination thereof). Network 1718 communicateswith various computing devices and/or other electronic devices. Network1718 is communicating with one or more computing systems 1720 and one ormore data sources 1722. The patient-specific spinal treatment,operations, and procedures module 1714 may access or may be accessed bycomputing systems 1720 and/or data sources 1722 through a web-enableduser access point. Connections may be a direct physical connection, avirtual connection, and other connection type. The web-enabled useraccess point may comprise a browser module that uses text, graphics,audio, video, and other media to present data and to allow interactionwith data via the network 1718.

Access to the patient-specific spinal treatment, operations, andprocedures module 1714 of the computer system 1702 by computing systems1720 and/or by data sources 1722 may be through a web-enabled useraccess point such as the computing systems' 1720 or data source's 1722personal computer, cellular phone, smartphone, laptop, tablet computer,e-reader device, audio player, or other device capable of connecting tothe network 1718. Such a device may have a browser module that isimplemented as a module that uses text, graphics, audio, video, andother media to present data and to allow interaction with data via thenetwork 1718.

The output module may be implemented as a combination of an all-pointsaddressable display such as a cathode ray tube (CRT), a liquid crystaldisplay (LCD), a plasma display, or other types and/or combinations ofdisplays. The output module may be implemented to communicate with inputdevices 1712 and they also include software with the appropriateinterfaces which allow a user to access data through the use of stylizedscreen elements, such as menus, windows, dialogue boxes, tool bars, andcontrols (for example, radio buttons, check boxes, sliding scales, andso forth). Furthermore, the output module may communicate with a set ofinput and output devices to receive signals from the user.

The input device(s) may comprise a keyboard, roller ball, pen andstylus, mouse, trackball, voice recognition system, or pre-designatedswitches or buttons. The output device(s) may comprise a speaker, adisplay screen, a printer, or a voice synthesizer. In addition a touchscreen may act as a hybrid input/output device. In another embodiment, auser may interact with the system more directly such as through a systemterminal connected to the score generator without communications overthe Internet, a WAN, or LAN, or similar network.

In some embodiments, the system 1702 may comprise a physical or logicalconnection established between a remote microprocessor and a mainframehost computer for the express purpose of uploading, downloading, orviewing interactive data and databases on-line in real time. The remotemicroprocessor may be operated by an entity operating the computersystem 1702, including the client server systems or the main serversystem, and/or may be operated by one or more of the data sources 1722and/or one or more of the computing systems 1720. In some embodiments,terminal emulation software may be used on the microprocessor forparticipating in the micro-mainframe link.

In some embodiments, computing systems 1720 who are internal to anentity operating the computer system 1702 may access thepatient-specific spinal treatment, operations, and procedures module1714 internally as an application or process run by the CPU 1706.

The computing system 1702 may include one or more internal and/orexternal data sources (for example, data sources 1722). In someembodiments, one or more of the data repositories and the data sourcesdescribed above may be implemented using a relational database, such asDB2, Sybase, Oracle, CodeBase, and Microsoft® SQL Server as well asother types of databases such as a flat-file database, an entityrelationship database, and object-oriented database, and/or arecord-based database.

The computer system 1702 may also access one or more databases 1722. Thedatabases 1722 may be stored in a database or data repository. Thecomputer system 1702 may access the one or more databases 1722 through anetwork 1718 or may directly access the database or data repositorythrough I/O devices and interfaces 1712. The data repository storing theone or more databases 1722 may reside within the computer system 1702.

In some embodiments, one or more features of the systems, methods, anddevices described herein can utilize a URL and/or cookies, for examplefor storing and/or transmitting data or user information. A UniformResource Locator (URL) can include a web address and/or a reference to aweb resource that is stored on a database and/or a server. The URL canspecify the location of the resource on a computer and/or a computernetwork. The URL can include a mechanism to retrieve the networkresource. The source of the network resource can receive a URL, identifythe location of the web resource, and transmit the web resource back tothe requestor. A URL can be converted to an IP address, and a DomainName System (DNS) can look up the URL and its corresponding IP address.URLs can be references to web pages, file transfers, emails, databaseaccesses, and other applications. The URLs can include a sequence ofcharacters that identify a path, domain name, a file extension, a hostname, a query, a fragment, scheme, a protocol identifier, a port number,a username, a password, a flag, an object, a resource name and/or thelike. The systems disclosed herein can generate, receive, transmit,apply, parse, serialize, render, and/or perform an action on a URL.

A cookie, also referred to as an HTTP cookie, a web cookie, an internetcookie, and a browser cookie, can include data sent from a websiteand/or stored on a user's computer. This data can be stored by a user'sweb browser while the user is browsing. The cookies can include usefulinformation for websites to remember prior browsing information, such asa shopping cart on an online store, clicking of buttons, logininformation, and/or records of web pages or network resources visited inthe past. Cookies can also include information that the user enters,such as names, addresses, passwords, credit card information, etc.Cookies can also perform computer functions. For example, authenticationcookies can be used by applications (for example, a web browser) toidentify whether the user is already logged in (for example, to a website). The cookie data can be encrypted to provide security for theconsumer. Tracking cookies can be used to compile historical browsinghistories of individuals. Systems disclosed herein can generate and usecookies to access data of an individual. Systems can also generate anduse JSON web tokens to store authenticity information, HTTPauthentication as authentication protocols, IP addresses to tracksession or identity information, URLs, and the like.

Although the embodiments discussed herein generally relate topatient-specific spinal treatment, operations, and procedures, thesystems, methods, and devices disclosed herein can be used for anynon-spinal patient-specific treatment, operations, and procedure aswell. Also, the systems, methods, and devices disclosed herein can beused with x-ray, MRI, CT, or any other imaging systems or devices thatproduce two-dimensional and/or three-dimensional medical image or videodata.

Although this invention has been disclosed in the context of certainembodiments and examples, it will be understood by those skilled in theart that the invention extends beyond the specifically disclosedembodiments to other alternative embodiments and/or uses of theinvention and obvious modifications and equivalents thereof. Inaddition, while several variations of the embodiments of the inventionhave been shown and described in detail, other modifications, which arewithin the scope of this invention, will be readily apparent to those ofskill in the art based upon this disclosure. It is also contemplatedthat various combinations or sub-combinations of the specific featuresand aspects of the embodiments may be made and still fall within thescope of the invention. It should be understood that various featuresand aspects of the disclosed embodiments can be combined with, orsubstituted for, one another in order to form varying modes of theembodiments of the disclosed invention. Any methods disclosed hereinneed not be performed in the order recited. Thus, it is intended thatthe scope of the invention herein disclosed should not be limited by theparticular embodiments described above.

Conditional language, such as, among others, “can,” “could,” “might,” or“may,” unless specifically stated otherwise, or otherwise understoodwithin the context as used, is generally intended to convey that certainembodiments include, while other embodiments do not include, certainfeatures, elements and/or steps. Thus, such conditional language is notgenerally intended to imply that features, elements and/or steps are inany way required for one or more embodiments or that one or moreembodiments necessarily include logic for deciding, with or without userinput or prompting, whether these features, elements and/or steps areincluded or are to be performed in any particular embodiment. Theheadings used herein are for the convenience of the reader only and arenot meant to limit the scope of the inventions or claims.

Further, while the methods and devices described herein may besusceptible to various modifications and alternative forms, specificexamples thereof have been shown in the drawings and are hereindescribed in detail. It should be understood, however, that theinvention is not to be limited to the particular forms or methodsdisclosed, but, to the contrary, the invention is to cover allmodifications, equivalents, and alternatives falling within the spiritand scope of the various implementations described and the appendedclaims. Further, the disclosure herein of any particular feature,aspect, method, property, characteristic, quality, attribute, element,or the like in connection with an implementation or embodiment can beused in all other implementations or embodiments set forth herein. Anymethods disclosed herein need not be performed in the order recited. Themethods disclosed herein may include certain actions taken by apractitioner, however, the methods can also include any third-partyinstruction of those actions, either expressly or by implication. Theranges disclosed herein also encompass any and all overlap, sub-ranges,and combinations thereof. Language such as “up to,” “at least,” “greaterthan,” “less than,” “between,” and the like includes the number recited.Numbers preceded by a term such as “about” or “approximately” includethe recited numbers and should be interpreted based on the circumstances(e.g., as accurate as reasonably possible under the circumstances, forexample ±5%, ±10%, ±15%, etc.). For example, “about 3.5 mm” includes“3.5 mm.” Phrases preceded by a term such as “substantially” include therecited phrase and should be interpreted based on the circumstances(e.g., as much as reasonably possible under the circumstances). Forexample, “substantially constant” includes “constant.” Unless statedotherwise, all measurements are at standard conditions includingtemperature and pressure.

As used herein, a phrase referring to “at least one of” a list of itemsrefers to any combination of those items, including single members. Asan example, “at least one of: A, B, or C” is intended to cover: A, B, C,A and B, A and C, B and C, and A, B, and C. Conjunctive language such asthe phrase “at least one of X, Y and Z,” unless specifically statedotherwise, is otherwise understood with the context as used in generalto convey that an item, term, etc. may be at least one of X, Y or Z.Thus, such conjunctive language is not generally intended to imply thatcertain embodiments require at least one of X, at least one of Y, and atleast one of Z to each be present.

What is claimed is:
 1. A system for providing intraoperative tracking toassist spinal surgery, the system comprising: two or more activesensors, wherein each of the two or more active sensors comprises one ormore accelerometers and/or one or more gyroscopes; two or moreattachment devices, wherein each of the two or more attachment devicescomprises one or more of said active sensors, a power source, and awireless transmitter, wherein the two or more attachment devices areconfigured to be attached to two or more vertebrae of a spine of apatient in a configuration such that two of three axes of position datato be collected by the two or more active sensors are on a plane assumedto be parallel, or substantially parallel with a determinate angle, to asagittal plane of the patient, wherein when the two or more attachmentdevices are attached to the two or more vertebrae of the spine of thepatient during a spinal surgery, each of the two or more active sensorsare configured to provide one or more position and and/or orientationdata of each of the two or more vertebrae of the spine of the patient towhich each of the two or more attachment devices are attached to; one ormore computer readable storage devices configured to store a pluralityof computer executable instructions; and one or more hardware computerprocessors in communication with the one or more computer readablestorage devices and configured to execute the plurality of computerexecutable instructions in order to cause the system to: receive the oneor more position and and/or orientation data from the wirelesstransmitter of each of the two or more attachment devices insubstantially real-time; dynamically determine, based at least in parton the one or more position and orientation data and using gravity as acommon reference among the one or more position and orientation datareceived from the wireless transmitter of each of the two or moreattachment devices, the one or more position and and/or orientation ofthe two or more vertebrae of the spine of the patient to which the twoor more attachment devices are attached to; dynamically generate one ormore performance metrics for the spinal surgery based at least in parton comparing the determined one or more position and orientation of thetwo or more vertebrae of the spine of the patient to which the two ormore attachment devices are attached to with a predetermined surgicalplan for the patient, wherein the predetermined surgical plan comprisesa desired curvature of a patient-specific spinal rod for implantation tothe spine of the patient and one or more desired position andorientation data of the two or more vertebrae of the spine of thepatient, wherein the desired curvature of the patient-specific spinalrod for implantation to the spine of the patient and the one or moredesired position and orientation data of the two or more vertebrae ofthe spine of the patient are determined by: accessing one or moremedical images of a pre-operative spine of the patient; accessing datarelated to one or more previous spinal surgeries that were performedprior to the spinal surgery, wherein the accessed data related to theone or more previous spinal surgeries comprises: a curvature of animplanted patient-specific spinal rod, one or more position andorientation data of two or more vertebrae of the one or more previousspinal surgeries, and data related to one or more previous surgeriesperformed by a surgeon performing the spinal surgery for the patient,analyzing the accessed one or more medical images of the preoperativespine of the patient; modifying the accessed one or more medical imagesof the preoperative spine of the patient to simulate an outcome of thespinal surgery for the patient, wherein the accessed one or more medicalimages of the preoperative spine of the patient are modified based atleast in part on the accessed data related to the one or more previousspinal surgeries; and determining, based at least in part on themodified one or more medical images of the pre-operative spine of thepatient, the desired curvature of the patient-specific spinal rod forimplantation to the spine of the patient and the one or more desiredposition and orientation data of the two or more vertebrae of the spineof the patient; and dynamically generate, based at least in part on thegenerated one or more performance metrics, guidance instructions forperforming the spinal surgery.
 2. The system of claim 1, wherein the oneor more active sensors comprise an inertial measurement unit with sixdegrees of freedom.
 3. The system of claim 1, wherein the one or moreactive sensors comprise an inertial measurement unit with nine degreesof freedom.
 4. The system of claim 1, wherein the two or more attachmentdevices comprises a vertebral anchor.
 5. The system of claim 1, whereinthe two or more attachment devices comprises a vertebral screw.
 6. Thesystem of claim 5, wherein the vertebral screw is a mono-axial screwcomprising at least one sensor.
 7. The system of claim 5, wherein thevertebral screw is a poly-axial screw comprising at least one sensor. 8.The system of claim 1, wherein the two or more attachment devicescomprises a surgical tool.
 9. The system of claim 8, wherein thesurgical tool comprises a screwdriver or nutdriver.
 10. The system ofclaim 1, wherein two or more attachment devices are reusable.
 11. Thesystem of claim 1, wherein the preoperative surgical plan is developedat least in part by an artificial intelligence system.
 12. The system ofclaim 1, wherein the one or more medical images of the pre-operativespine of the patient comprises a sagittal x-ray image of thepre-operative spine of the patient.
 13. The system of claim 1, whereinthe predetermined surgical plan further comprises a desiredpost-operative spinal curvature of the patient.
 14. The system of claim1, wherein the two or more attachment devices comprises a portion of avertebral screw.
 15. The system of claim 14, wherein the portion of thevertebral screw is adapted to be broken off prior to completion of thespinal surgery.
 16. The system of claim 1, wherein the system is furthercaused to periodically generate guidance instructions for performing thespinal surgery.
 17. The system of claim 1, wherein the system is furthercaused to continuously generate guidance instructions for performing thespinal surgery until a spine of the patient is adjusted to apre-determined acceptable level.
 18. The system of claim 1, wherein theaccessed data related to the one or more previous spinal surgeriescomprises data related to one or more previous surgeries of one or moreother patients.
 19. The system of claim 1, wherein the accessed one ormore medical images of the pre-operative spine of the patient aremodified further based at least in part on one or more preferences of asurgeon performing the spinal surgery for the patient.
 20. The system ofclaim 1, wherein one or more of the one or more previous surgeries arerelated to one or more surgeries of one or more patients other than thepatient that were performed prior to the spinal surgery.